Plans as resources for action (Suchman, 1988)

Two ways of thinking about practice put (i) “plans as determinants of action”, and (ii) “plans as resources for action”. The latter has become a convention, particularly through research into Human Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW).

While the more durable explanation appears the Suchman (1987) book (specifically section “8.2 Plans as resources for action”, pp. 185-189), a source more readily at hand may be found in a Suchman (1988) article.

4. Plans as resources for action

Taken as the determinants of what people do, plans provide both a device by which practice can be represented in cognitive science and a solution to the problem of purposeful action. If we apply an ethnomethodological inversion to the cognitive science view, however, plans take on a different status. Rather than describing the mechanism by which action is generated and a solution to the analysts’ problem, plans are common sense constructs produced and used by actors engaged in everyday practice. As such, they are not the solution to the problem of practice but part of the subject matter. While plans provide useful ways of talking and reasoning about action, their relation to the action’s production is an open question. [….] [p. 314]

The planning model takes off from our common sense preoccupation with the anticipation of action and the review of its outcomes and attempts to systematize that reasoning as a model for situated practice itself. These examples, however, suggest an alternative view of the relationship between plans, as representations of conditions and actions, and situated practice. Situated practice comprises moment-by-moment interactions with our environment more and less informed by reference to representations of conditions and of actions, and more and less available to representation themselves. The function of planning is not to provide a specification or control structure for such local interactions, but rather to orient us in a way that will allow us, through the local interactions, to respond to some contingencies of our environment and to avoid others. As Agre and Chapman put it “[m] ost of the work of using a plan is in determining its relevance to the successive concrete situations that occur during the activity it helps to organize” (1987a). Plans specify actions just to the level that specification is useful; they are vague with respect to the details of action precisely at the level at which it makes sense to forego specification and rely on the availability of a contingent and necessarily ad hoc response. Plans are not the determinants of action, in sum, but rather are resources to be constructed and consulted by actors before and after the fact. [pp. 314-315]

Suchman (1987)
  • Agre, P., and Chapman, D. (1987a). What are plans for? Paper presented for the panel on Representing Plans and Goals, DARPA Planning Workshop, Santa Cruz, CA., MIT Artificial Intelligence Laboratory, Cambridge, MA.
  • Agre, P., and Chapman, D. (1987b). Pengi: An implementation of a theory of activity. Proceedings of the American Association for Artificial Intelligence, Seattle, WA.


The best time to plant a tree was twenty years ago

Does “the best time to plant a tree was twenty years ago and the second best time is now” date back further than 1988?

It is time to look long and hard at the value of the urban forest and create the broad-based efforts — in research, funding and citizen participation — needed to improve it. The lesson is, the best time to plant a tree was twenty years ago and the second best time is now.

Moll (1988), p. 41

In 1998, Gary Moll was president with the American Forestry Association. He was recognized in “Gary Moll Wants People and Nature to Work Together” | Fall 2009 | ESRI ArcNews Online. In 2013, he was coauthor of “Shading Our Cities” | Island Press.

The rising prices of Christmas trees in 2019 surfaced this question.

Consumers on the hunt for a Christmas tree have little to cheer about this year, as prices are through the roof due to a shortage of trees that can be traced back to the 2008 financial crisis.

The Great Recession put thousands of American Christmas tree farmers out of business, resulting in far fewer seedlings being planted. As trees have a maturity cycle of 10 years, the lack of supply is just now beginning to bite, pushing up U.S. demand for Canadian Christmas trees and causing higher prices for consumers across the continent. [….]

Paul Quinn suspects the supply shortage will remain for at least a couple years.

“As the economics get better for tree growers you’ll see them planting more trees. Unfortunately, you had to have that foresight 10 years ago,” he said.

Reynolds (2019)

In the pursuit of etymology and the better quote citation, I would welcome seeing earlier uses of the phrase!


#tree, #years-ago

2019/11/05 13:15 “Barriers to Data Science Adoption: Why Existing Frameworks Aren’t Working”, Workshop at CASCON-Evoke, Markham, Ontario

Workshop led by @RohanAlexander and @prof_lyons at #CASCONxEvoke on “Barriers to Data Science Adoption: Why Existing Frameworks Aren’t Working“, with the following abstract.

Broadly, data science is an interdisciplinary scientific approach that provides methods to understand and solve problems in an evidence-based manner, using data and experience. Despite the clear benefits from adoption, many firms face challenges, be that legal, organisational, or business practices, when seeking to implement and embed data science within an existing framework.

In this workshop, panel and audience members draw on their experiences to elaborate on the challenges encountered when attempting to deploying data science within existing frameworks. Panel and audience members are drawn from business, academia, and think-tanks. For discussion purposes the challenges are grouped within three themes: regulatory; investment; and workforce.

The regulatory framework governing data science is outdated and fragmented, and for many new developments, regulations are in a state of flux, or non-existent. This creates an uncertain environment for investment in data science and can create barriers to the widespread adoption of state-of-the-art data science. For instance, the governance of data use and data sharing are unclear, and this may compromise trust in data. Additionally, privacy laws, currently under scrutiny in many countries, may limit how firms can use data in the near future affecting innovation, and planned investments (e.g., Google Sidewalk). As data science technologies and applications change rapidly, the regulatory framework must continually evolve or risk becoming outdated and a hindrance to developments in the field.

Investment risk exists for any project, however data science projects are especially risky for various reasons, including the fundamental role that datasets play. Creating, cleaning, updating, and securing a dataset is a difficult process that requires a substantial investment of resources. And while these are essential processes in order to extract value from data science, they rarely provide value themselves which can be a challenge when making a business case and investment decision and adds risk to the decision to adopt data science practices especially for small- and medium-sized businesses.

The workforce challenges of data science are extensive. It is difficult to recruit qualified candidates due to the specific skill sets needed, and, with more firms seeking to implement the new innovations, this problem is expected to become worse. Additionally, many fear the lack of diversity in the current pool of workers may hinder progress in cases where the data science applications are context specific and would benefit from subject-matter expertise and a diversity of experience.

Outcomes of the workshop are expected to include a report that lists a set of existing practises and high-level barriers to deployment.

Intro from Rohan Alexander (UToronto iSchool), co-organized with, Kelly Lyons (UToronto iSchool), Michelle Alexopoulos (UToronto Economics), Lisa Austin (UToronto Law)

Data science adoption doesn’t seem to have changed, over the past 5 to 10 years

Three themes:

  • Legal frameworks, consent issues, interacting with other jurisdictions
  • Organization challenges:  Difficult to add to old organization, lack of qualified candidates, lack of diversity, pipeline issue of graduates going to other countries
  • Risks:  Have to get clean datasets, so rational at 5% makes sense, or allocation of resources?

Submit questions to, #L763

This digest was created in real-time during the meeting,based on the speaker’s presentation(s) and comments from the audience. The content should not be viewed as an official transcript of the meeting, but only as an interpretation by a single individual. Lapses, grammatical errors, and typing mistakes may not have been corrected. Questions about content should be directed to the originator. The digest has been made available for purposes of scholarship, posted by David Ing.

Panel discussants

CASCONxEvoke Workshop Panel
CASCONxEvoke Workshop Panel

  • Launched by Deloitte 5 years ago
  • Ran survey, four themes
  • Found 16% adoption of AI in industry
  • 1. Lack of understanding:  Only 5% of Canadians think that they will be impacted by AI over the next 5 years, despite having smartphone.
  • 2. Lack of trust:  Data breaches, misuse of data.  Killer robots, not what machine learning is about.  Boston Dynamics video creates misconceptions.  Also chatbots used in customer care, fancy versions of press 1 for this, press 2 for that, yet people use terms like “computers are seeing”.  Computer systems as ominipresent, and don’t trust decision-makers.
  • 3. Lack of awareness:  In Toronto, ecosystem of startups, but difficult from them to link to enterprise companies.  Not getting in front of decision-makers.  Enterprises feel risk of dealing with startups that may not be around for few years.  Hard to advertise, misuse of language.
  • 4. Inability to scale:  Companies don’t know how to adopt.  May hire data scientists, but into corner, and think they’ll do cool stuff and make money.  Have to think of ROI from beginning.  May not have incentives to put into production, after the work is done.  Prove to me it works, versus assume that it’s going to work.

Ajiolomohi Egwaikhide, Senior Data Scientist, IBM Systems

  • What can go wrong?  Bad algorithm, or bad data
  • Customers want to take data, and too cool stuff, but don’t have enough data or right data to solve business problem.  Then end up with backlash.
  • Bad data: 
    • a. Insufficient quantity
    • b. Non-representative training data, or data isn’t telling them what they’re thinking.
    • c. Quality of data, has a lot of outliers, noise, missing data.  Don’t know what they should be collecting.
    • d. Irrelevant features:  Lots of columns of database, but no business capabilities around them
  • Bad algorithm:
    • a. Using fancy algorithms instead of simple models, e.g. survivor algorithm versus simpler logistic regression.  Not selling the right thing. 
    • b. Underfitting
  • People jumping into data

Inmar Givoni, Uber Self-Driving Automobile Division

  • Haven’t defined adoption.
  • John McCarthy said if it works, don’t call it artificial intelligence.
  • There’s a lot of adoption, e.g. a smartphone has 100 instances of what we might call AI.
  • Legal aspects:  e.g. supervised deep learning algorithms, in medical imaging, but then issues with privacy and disagreements from experts on labels, should otherwise be solvable.
  • Risks:  Idea of killer robots.  Self-driving paradox, if get 10% improvement, would have 1.2M die instead of 1.3M, isn’t a personalized argument.
  • Technical:  From software engineering, coding algorithm, get a precision or metric of interest, you could have messed up, you wouldn’t know, because it’s not testable in the same way as regular software.  If can tune parameters, if you don’t have a deep understanding or mathematical intuition, will get people throwing data at it.  Irresponsible use.
  • Algorithms (e.g. Tensorflow) are still experimental, missing debugging, control flow.
  • Policy:  Technology ahead of law.  Ethical considerations, e.g. people messing up traffic signs.  Will continue working on robustness, but people should go to jail for tearing down a traffic sign.
  • Productionization:  Have data scientists, prototype quickly in a sandbox environment, load, train metrics, and they say it will work.  But then to put into a production system, it’s streaming and works in real time.  It doesn’t care about models, it cares about output and costs.  e.g. build a detector 5% better, but then the car doesn’t work as well.  Not good correlation between model-level metric and system-level metric.

Legal perspective (Aaron?)

  • Barrier to adoption:
  • (i) Regulatory:  Laws are antiquated.  Cambridge Analytica, etc., is based on the consent-based model.  People don’t read the terms they click on.  Transparency.  Dealing with disclosure.  We don’t know what we’re agreeing to.
  • (ii) Investment and risk:  Big undertakings, expensive.  Senior management vs. data scientists.  Companies treating data science as just another project.  Data quality.  Considered in a silo.  No architecture for data.
  • (iii) Workforce, trading and labour market:  Requires a lot of expertise, there’s a shortage of people.  Difficult to recruit people with skills.  Expect to become worse.  Lack of qualified labour.
  • Are there ways that universities could be more involved?  Can we build universities, or training protocols, within companies?
  • Data science will become more important, not less.  How to handle?

Panel discussion together.

Clients coming for consultants, because they want to push something forward, and say we can do it ourselves.  Data scientists put into a position to just solve this.

Bad reputation on trust and ethics.

  • Data science equated to steam engine and electricity.
  • Clients aware that they should be doing something.
  • What, but then trust and ethics.
  • In banking, lots of accountability associated with models in production.
  • Line between statistics and machine learning is blurry.
  • Struggling with black box approaches
  • Lack of trust slows us down
  • How can I do it?

Question:  Reputation of data science, and how it impacts work?

When are we going to get the cars off the road?

  • Companies are heavily invested, but it’s an open-ended scientific problem.
  • We don’t know how to do it, but then companies say we’re going to have it by end of the year.
  • Already have assistive technologies that work well if someone is behind the wheel
  • Robustness at 99.9% in a lot of technologies today
  • But have a lot of variability today.
  • If have someone behind wheel that could save from serious errors, or on given route, or under weather conditions, different.

Question:  Unsolvable problems.  Researchers show examples of recognizing cat from dog, but then expect we can do cars.  Problem of lack of understanding, but it’s not zero-one.  They’re feeling overconfident.  A general vision of what AI could do, but we’re not there today.

Interdisciplinarity is complicated.  Executives not making bad decisions, requisite understanding.  It’s economic, technical, trade, privacy, transnational.  Evolutionary, not binary on-off.  Will get to better decision-making frameworks, but will take time.

Question:  Reputation.  AI is doing their job, not true, could augment.  How to correct messaging?

In high school, start of robotics, were promised 4-day work weeks.  Predict that there will no such thing as replacement.

People’s jobs change over time.  Agriculture.  Call centres are replaced by chatbots.  If remove 80% of mundane work, but unemployment work isn’t 50% today from agricultural.  Problem is over what time frame, 5 years or 15 years.

Question:  Automation versus AI?

In school, used term machine learning instead of AI.  Now AI is everything.  Lots of natural language processing.  Technologies are getting more intertwined.

People myopic about technology.  Many jobs get created in unpredicted ways, for non-technical people.  Gig economy.  How many people get married through online dating apps?  Using AirBnB, Uber, are rapid changes in life.

People thinking about how should retrain.  Retraining programs by Bank of Canada aren’t being used.

Questions:  From Twitter — It’s AI when you’re funding.  It’s machine learning when you’re building.  It’s logistic regression when it’s implemented.

First course in machine learning including logistic regression and linear regression.  If you want to call it AI, call it AI, it doesn’t matter any more.

Question:  Mining sector, predictive maintenance.  Anti-fraud, in banking, 80% of workload is logistic regression. 

If you can structure data in a table, use logistic regression.  Get stability, robustness, conversion. 

Educate customers towards getting real value.

History, neural networks became popular, due to availability of data.  Successful on a very small set of applications.  Anything that a human being can do quickly, video-audio-words.  But have age or income, probably won’t get a neural network that works better than logistic regression.  But neural networks would solve problems that weren’t solvable on logistic regression.  Reputation, but then people trying to use neural networks where they don’t apply, in video or audio types.

Question:  Domain-specific knowledge?

Clients bring a lot of domain-specific knowledge.  Haven’t been in a situation where they’ve asked for a specific algorithm.  It has to make business sense for them.

Working with data, have to understand that data.

Question:  16% adoption rate?

16% adoption rate in Canada, in small and large businesses.

Question:  Trust.  Worry about lack of regulations?  Moving target? Locking things down?

Smaller companies don’t think about regulatory aspects.  Larger companies are working with regulators.  Trust issue.

Decisions that are made will become more important.  e.g. judge will make judgement, then can verify, and subject to review.  A construct to review black box decisions?  Proprietary?  Can’t review?  Mortgage applications, university applications, bail applications.

Question:  Regulation.

In Canada, sometimes not taking care of own, e.g. GDPR is in Europe.

Question:  Not releasing datasets.  Understanding why AI is making decisions.

A lot of companies think their data is their competitive advantage.  But at the same time, want to get access to others’ data, so have to share.  Startups in Toronto work on how might share insights without sharing data.

For self-driving, Waymo isn’t shared.  Trying to figure the best way to go at it.  Tough.  Sensitivity to cyber attacks.

Emergence of three data blocks of governance. 

  • Geo-political development.  China, U.S. and EU moving different ways, other countries haven’t moved.  Would like to see Canada take leadership position.
  • Economic:  85% of top company value is intellectual capital and brand value.

Regulations under constant review?

Lawyers take a principled approach.  But things are moving quicker.  e.g. data portability, is it mine or company?  IoT and sensor data is at the bleeding edge.  CCTV cameras that got hacked.  Measure twice and cut once.

AI is a marketing word, used badly.  1960s-1970s interpretation of human consciousness.  Younger interpretation of things that do things for you, not conscious.  Executives want adoption, but not definitions.  Black box, magic.  When asked about adopting AI, are they adopting heuristic algorithms that marketers would call it.

What problem are we trying to solve?  Rate of adaptation, faster or better?  Pace of activity is right?  Whose problem are we trying to solve?  In Canada, have banks, medium-size manufacturing, and lots of small.  Need to find a way to have conversations with IT organization, as only going to give budget.  Technology has enabled a small number of companies to decimate government, control the way we’re living.  Have to look at open source, and then governments take control?

Is there something we can do, if there’s a barrier.

Data science as science.

Research money earmarked as part of IT budget.

Question:  Policy in other jurisdictions?

Transnational, also in NAFTA 2.0.  Constraints by other countries.  If want to set own policies, it’s about economic opportunity.  Don’t want to set up a regulatory framework where companies can’t operate here.

Question:  As users, might we own our own data?

What your phone does, while you’re asleep.  The amount of world knows about you isn’t good.

Trying to write a research proposal, going forward.  How should we approach?

Panel not right format?

Closing, 1 minute each.

Great technology, fourth industrial revolution, will make changes, do have to approach with caution.

What is machine learning useful for, what isn’t it useful for?  It’s mixed up.

AI, ML, data science — it’s the future, right conditions.  Need to do more education of ourselves.  There’s a lot we don’t know.

We do get to create a policy framework.

#artficial-intelligence, #cascon, #data-science, #machine-learning

Own opinion, but not facts

“You are entitled to your own opinions, but not to your own facts” by #DanielPatrickMoynihan is predated on @Freakonomics by #BernardMBaruch 1950 “Every man has a right to his own opinion, but no man has a right to be wrong in his facts”.
Source: “There Are Opinions, And Then There Are Facts” | Fred Shapiro | August 18, 2011 | Freakonomics Blog at

See also:

Bernard Baruch, photographed by Harris & Ewing,,_BERNARD_2.jpg

#facts, #opinions

R programming is from S, influenced by APL

History of data science tools has evolved to #rstats of the 1990s, from the S-Language at Bell Labs in the 1970s, and the <- arrow symbols came from #APL (A Programming Language) in the 1960s.

As you all know, R comes from S. But you might not know a lot about S (I don’t). This language used <- as an assignment operator. It’s partly because it was inspired by a language called APL, which also had this sign for assignment.

But why again? APL was designed on a specific keyboard, which had a key for<-:

“Why do we use arrow as an assignment operator?” | Colin Fay | September 24, 2018 at

In the mid-1980s, I worked primarily in APL, with the special character set keyboard.

By User:Rursus – APL-keybd.svg, CC BY-SA 3.0,

I was curious about this, because I saw the alternative to <- with an “assign” function.

A more laborious, though sometimes necessary, way to assign variables is to use the assign function.

assign(“j”, 4)
[1] 4

4.2.1. Variable Assignment” | Jared P. Lander | R for Everyone: Advanced Analytics and Graphics

This suggests that an alternative to using the arrow is a more functional style of programming.

#apl-a-programming-language, #r-programming, #s-programming-language

Bullshit, Politics, and the Democratic Power of Satire | Paul Babbitt | 2013

Satire can be an antidote, says Prof. #PaulBabbitt @muleriders , to #bullshit (c.f. rhetoric; hypocrisy; crocodile tears; propaganda; intellectual dishonesty; politeness, etiquette and civility; commonsense and conventional wisdom; symbolic votes; platitudes and valence issues).

While lying is a misrepresentation of the truth, [Harry] Frankfurt argues, BS is an indifference to truth and a misrepresentation of the self—and worse than lying.

[…] BS is not only deceptive but also contributes to the decay of public discourse. Its emptiness, its meaninglessness crowd out substantive discussion. It directs attention to the trivial as much as the false, and it dumbs us down. Unlike the lie, BS derives its effectiveness from the way it says nothing while appearing to say something profound. […]

There’s no cure for BS, but there is a powerful treatment: satire, which can identify and mock BS, resistant as it is to conventional modes of argumentation and dispute. At its best, satire exposes the pretensions of the powerful. Irreverence sometimes troubles us, but irreverence, or at least the tension between reverence and irreverence, is essential to democracy. Reverence inspires an adherence to authority that is undemocratic at its core. In challenging authority, humor performs a critical democratic duty.

Babbitt, Paul. 2013b. “Taking BS Seriously.” The Chronicle of Higher Education, November 18, 2013.
Illustration by @bloch_serge, in Babbitt 2013b, The Chronicle of Higher Education

The short article in The Chronicle of Higher Education (November 2013) was preceded by a longer article for the American Political Science Association (August 2013).

If the opposite of bullshit is sincerity, then it may seem odd to offer satire as counter-measures. The mechanisms of exposing bullshit come close to bullshit itself. Satire is, after all, insincere. Stephen Colbert of the Colbert Report is a transparent persona that seems to have little to do with the actual person Stephen Colbert. Because it is transparent, though, it would be unfair to accuse Colbert of practicing bullshit in the same way that Callicles does. We may distinguish easily between the obvious and transparent performance of Stephen Colbert and the dissembling of a politician. […]

Humor may be mean spirited and cruel. However, to object to comedy because it is irreverent, because it challenges authority is to deny its most important power. It is precisely those modes that have the best chance of exposing bullshit to the ridicule it deserves. […]

There are good reasons to use such a strategy, and they can help us understand the purpose of satire in our own political environment. The fact that the satirist may pay with her life is perhaps the best evidence we have of its subversive quality. Satire sometimes provokes feelings of violation and violent reactions. Satire is irreverent, and they may target things you or I hold sacred. [….]

The only rule the bullshitter follows is to say anything so long as it works. As long as the bullshitter refuses to abide by standards of transparency and honest exchange of ideas then there is no choice but to engage the bullshitter on different ground. The satirist does not follow the same rules as the serious journalist or pundit. In the main, political humor is cast as critical, even destructive precisely because the humorist does not play by the same rules as “respectable” journalists. The rule breaking characteristic of the satirist is an important element in satire’s subversive character. It is not just that the satirist is targeting important powers of the system, it is that the satirist does not follow the rules either. [….]

There is of course an ugly side to this—a mass, largely uninformed audience may not be able to distinguish between exposing bullshit and mocking serious and sophisticated arguments—pomposity is to an extent a subjective evaluation of others. Comedy is indiscriminate in its targets. It not only poses questions, but it subjects its targets to ridicule. In its leveling, it erases distinctions and hierarchies that in fact are important
elements in any human society. (McWilliams 1995) The informed and concerned public servant is as likely to be ridiculed as the most foolish politician. Comedy can and does expose the tendency of the public to follow the lowest common denominator. Furthermore, the kind of comedy most citizens will see, hear, or read has entertainment as its primary purpose. It does not escape the commercial imperative. One should suspect that if there is a choice between making money and performing a civic duty, money making will win out.

Babbitt, Paul. 2013a. “Bullshit, Politics, and the Democratic Power of Satire.” In American Political Science Association 2013 Annual Meeting. Chicago.


Babbitt, Paul. 2013a. “Bullshit, Politics, and the Democratic Power of Satire.” In American Political Science Association 2013 Annual Meeting. Chicago.

Babbitt, Paul. 2013b. “Taking BS Seriously.” The Chronicle of Higher Education, November 18, 2013.

Frankfurt, Harry G. 2009. On Bullshit. Princeton University Press.

#bs, #satire

Health Systems Research and Critical Systems Thinking: The case for partnership | Michael C. Jackson, Luis G. Sambo | 2019/08

If we don’t first know “what is system is”, how do we approach an intervention? #MichaelCJackson OBE and Dr. #LuisGSambo appreciate the difference between “systems thinking” (plural) and “system dynamics” (singular), and suggest expanding theory with Critical #SystemThinking in Health Systems Research.

An ignorance of history is, if anything, even more pronounced among those authors in [Health Systems Research] influenced by complexity theory and the concept of ‘complex adaptive systems’. [….]

Most authors employing complexity theory in HSR seem to believe that it sprung forth fully formed from nothing or has somehow supplanted other bodies of work in systems thinking.

Such a poor appreciation of the history makes it almost inevitable that HSR will draw upon a restricted part of the systems and complexity tradition in developing its theories. In fact, it is the system dynamics and ‘complex adaptive systems’ strands that have come to dominate HSR at the expense of others. [….]

The problems start because of a lack of clarity in HSR about what type of ‘dynamic entity’, or ‘system’, a health system is. [….]

[Critical Systems Thinking] takes a radically different approach to HSR in the way it responds to the complexity encountered in the health systems domain. Primarily, HSR designates health systems as ‘complex adaptive systems’, and then looks to system dynamics to provide knowledge of their inner workings and supply insights into how they can best be managed. [….]

CST by contrast regards ‘messes’, like those found in public health, as ‘unknowable’. They give rise to what Rittel and Webber call ‘wicked problems’, which are intractable for decision-makers … [….]

CST, therefore, bypasses the issue of what kind of ‘system’ a health system is by stating that we will never know. Far from being a negative strategy, however, this opens up a whole new realm of possibilities, …

CST can assist … by referring to the three commitments of CST – ‘critical awareness’, ‘pluralism’, and ‘improvement’.

Source: Jackson, Michael C. & Sambo, Luis G. (2019). Health systems research and critical systems thinking: the case for partnership. 10.13140/RG.2.2.36160.48648.

#critical-systems-thinking, #systems-thinking