Bibliographic record
Abstract
Abstract Afew guidelines usually govern my activities when I conduct case study research: (a) per-the person who is the object of my investigation to take the lead as far as he or she is able to go, (b) suspend any notions regarding how the study will work out, (c) be open to surprises,(d) use the problems that arise as opportunities to learn about and welcome developments in surrounding disciplines, and (e) estimate how much time it will take to complete the project, triple it, and be prepared to triple it again. I didn ‘t think I would need to consult my guidelines when I embarked on a study of James M. Barrie. I intended it to be a simple investigation with a specific focus on the possible Oedipal origins of Barrie ‘s famous story about Peter Pan. My original purpose went no further than to locate and organize some new material for a course in personality psychology that I have taught for many years. In the context of a field deeply rooted in the tradition of psychometric science wherein variables instead of people are the preferred units of analysis, I felt it would do no harm to expose my students to “old “ ways of thinking about personality development, if only in the form of interludes or breaks between lectures on scale construction and research designs.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.126 | 0.059 |
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".