Bibliographic record
Abstract
Beliefs and practices are obviously intertwined. Much of what we do, we do because of beliefs we hold. Most obviously, we do many things because we believe that they are worth doing . But the reverse is also true: our practices also shape our beliefs. This can happen, for example, because we embrace beliefs that justify our actions. Aristotle observed that ‘those who have done a service to others feel friendship and love for those they have served’ ( Ethics , ¶1167b). Note the causal direction: from doing a service, to warm feelings. Carol Tavris and Elliot Aronson use a striking metaphor to describe how conviction grows in the wake of our choices. A person facing a momentous yet uncertain decision is perched on the apex of a pyramid. Having chosen one way or the other, rationalization kicks in, and the person slides down one side of the pyramid or the other, becoming ever more distant from the person they would have been had they chosen otherwise. ‘By the time the person is at the bottom of the pyramid’, Tavris and Aronson comment, ‘ambivalence will have morphed into certainty, and he or she will be miles away from anyone who took a different route’ (2007, 33). Actions can shape beliefs in more indirect ways as well. The act of entering a particular social milieu, such as a new organization, will over time affect our network of beliefs. We can thus think of practices and beliefs as constituting a wider network than that of beliefs alone. Throughout this work, we have noted various characteristics of the network of beliefs. We can assume that the broader network, which includes practices, shares these qualities. Let us now examine some other implications of this broader network of practices and beliefs. Means and ends The world is not neatly chopped into simple means and ends. Certain practices pursue multiple ends, and some things are both goods in themselves and means to other goods. We will explore the implications of this claim in an unusual way, by considering the position of someone who denies it. ‘In any given person's value system’, argues policy theorist Ralph Ellis, ‘there are literally thousands of extrinsic values’. But ‘there are only a very few things that could possibly be construed as valuable for their own sake’ (1998, 12).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".