Knowledge as a Public Good : Comments on Special Issue: What is the Good University?
Bibliographic record
Abstract
The articles in the recent Special Issue of Philosophical Inquiry in Education are a welcome sign that a growing number of philosophers of education are turning their attention to the functions of the institution in which they work.While there have been innumerable philosophical books and articles about elementary and secondary schools, until quite recently analyses of university education have, with some notable exceptions, been far rarer.Furthermore, the authors in this collection consider central issues in the nature of university education and research, all of which are conceptually connected to the goods that universities should offer.In particular, they analyze the following: an articulation of the relationship between the epistemic goals of the university and social justice that is not weakened by an over-emphasis on socioeconomic goods (Kotzee, 2018); the promotion of Indigenous knowledge and reconciliation with First Nations, and the extent to which this process is consistent with academic freedom (Tanchuk, Cruse, and McDonough, 2018); the advancement of a service conception of the university as a means to secure educational opportunities for those the market ignores (Martin, 2018b); a capabilities approach to wellbeing in the academy that goes beyond current quick fixes through the use of a philosophical framework that recognizes the distinctive norms and values of the institution (Gereluk, 2018); and an analysis of the strengths and weaknesses of the developmental university in Africa and elsewhere, particularly in how its epistemic goals can be undermined (McCowan, 2018).As Martin (2018a) points out in his introduction, the articles demonstrate in different ways the relevance of key concepts in the philosophy of education: "Authority, epistemic value, political justice, instrumental and intrinsic goods, human flourishing and autonomy" (p.115).To this extent they not only show the relevance of the discipline, but they move discussions away "from rationalization to argumentation" (p.113).The purpose of my commentary is to highlight a central theme of the Special Issue-namely, knowledge as a public good-and to explain the ways in which it is being undermined at universities in Canada.Foremost in the argumentation referred to above, it seems to me, is a consensus that the knowledge produced in universities is indeed a public good.Kotzee (2018), for example, argues that, The important point is that both education's instrumental and intrinsic value work through knowledge: by being educated, one comes to know something and, because of what one knows, one then becomes more likely to reap certain instrumental or intrinsic benefits (p.125)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.063 | 0.057 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".