Bibliographic record
Abstract
This paper explores the relations between self-esteem and competition. Self-esteem is a very important good and competition is a widespread phenomenon. They are commonly linked, as people often seek self-esteem through success in competition. Although competition in fact generates valuable consequences and can to some extent foster self-esteem, empirical research suggests that competition has a strong tendency to undermine self-esteem. To be sure, competition is not the source of all problematic deficits in self-esteem, and it can arise for, or undercut goods other than self-esteem. But the relation between competition and access to self-esteem is still significant, and it is worth asking how we might foster a desirable distribution of the latter in the face of difficulties created by the former. That is the question addressed in this paper. The approach I propose neither recommends self-denial nor the uncritical celebration of the rat race. It charts instead a solidaristic path to support the social conditions of the self-esteem of each individual. The paper proceeds as follows. I start, in section 2, by clarifying key concepts involved in the discussion. In section 3, I identify ten mechanisms that support individuals’ self-esteem and impose limits on competition. I focus, in particular, on the challenges faced by people in their practices of work. In section 4, I outline prudential and moral arguments to justify the use of the proposed mechanisms. Section 5 concludes with remarks on the role of social criticism in the processes of change implementing the mechanisms.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".