Bibliographic record
Abstract
If I promise to pick you up at the airport, I thereby become obligated to do so. But this is not the only way I could undertake this obligation. If I offer to pick you up, and you accept my offer, I become obligated to pick you up in much the same way. I would also undertake similar obligations if you asked me to pick you up and I accepted your request, or if we made an agreement that I will pick you up at the airport and in exchange you’ll buy me dinner. Why are the normative effects of accepted offers, accepted requests, and agreements so similar to those of promises? I argue that theorists of promising need to answer this question, and so they need to pay attention to offers, requests, and agreements. On the theory I defend, promises, offers, requests, and agreements have such similar normative effects because they all result in joint decisions between the relevant parties. I argue that this ‘joint decision view’ provides an attractive explanation of the similarities and differences between promises, offers, requests, and agreements.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".