Networking Western Psychology's Elite: A Digital Analysis of "A History of Psychology in Autobiography"
Bibliographic record
Abstract
This thesis analyzes digital social networks for the institutional affiliations of the one hundred and twenty authors in the A History of Psychology in Autobiography (AHPA) book series. \nThe introductory section contextualizes the analyses for the nine volumes in terms of the series’ historiographic foundations, socio-historical influences, and a history of the production of the first volume. It asserts that the series editors’ privileged disciplinary positions and the series’ unusual historiographic features render it an unusually precise internalist historical record of elite perspectives. The analytical chapter forwards the position that the AHPA networks illustrate the accuracy of Kurt Danziger’s (2006) historical premise of intellectual ‘centers’ and ‘peripheries’ in Western psychology’s disciplinary geography. The conclusion includes an assessment of the digital methods used, consideration of future directions, and a critical discussion of the AHPA series and how this thesis fits into a larger framework of ethical historiography.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".