Bibliographic record
Abstract
I began my university studies in Canada in international relations, so I took political science and economics courses in my first year. At the time, it seemed like my professors believed that in going to war, individuals (leaders) made choices to attain objective benefits and avoid costs (e.g., to secure an oil reserve). The glaring emotionality and the experiences of everyday life for most people in war, to me, were overlooked: the way that groups and group identities changed perceptions of what was “rational;” the collective, negotiated, moral aspects of these choices (Tausch et al., 2011; Thomas, McGarty, & Mavor, 2009). With relief, in my second year I discovered social psychology and then peace psychology. These were disciplines within which I felt I could articulate a model, and engage with empirical evidence, that groups teach members socially learned costs and benefits, including about the utility or futility of war. (e.g., Christie & Louis, 2012; Louis, 2008; Louis, 2009a, 2009b; Louis & Taylor, 2002; Thomas & Louis, 2013). Also, some types of behavior are not well explained by rational cost-benefit analysis models.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".