Bibliographic record
Abstract
Introduction “Friends make gifts and gifts make friends”, an Inuit proverb says. This pair of relations proposes a reason for reciprocity in giving when taken in reverse order, and in friendship, or liking one another, when taken in this order. However, not all gifts make friends, only friendly ones. Hence, one may in fact only have “friendship makes friendship”, a direct reciprocity in sentiment, plus the easily understandable “friends make gifts” and a possible role of giving for informing about liking. Staying with folk wisdom, this is what a beautiful old song expresses in saying J'aimerai qui m'aimera (I will love him who loves me). Nevertheless, although folk wisdom epitomizes respectable experience, it is no substitute for explanation. If “liking elicits giving” is rather straightforward, “receiving a gift elicits liking the giver” requires closer explanation and analysis of its conditions, and “liking elicits liking” still more so. These reciprocities based on liking constitute one of the main fields in the realm of reciprocity (along with balance reciprocity and self-interested continuation). Moreover, reciprocity in liking is a main social bond, with essential manifestations from general sociality to family love, the constitution of groups, and the nature of communities. Liking reciprocities also have a major direct normative value for the quality of society and of the relationships and people in it. Hence, understanding and explaining liking reciprocities is a major task.
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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 | 0.002 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".