Review of: "An Ontological Turn for Psychology in the age of the Machine and Global Warming"
Bibliographic record
Abstract
Potential competing interests: No potential competing interests to declare.This is an interesting article that focuses on an area of contemporary academic knowledge that is definitely in need of a decolonizing perspective (positivist psychology).Showing how indigenous perspectives intersect with the study of ontology (especially in the work of Heidegger, Arendt and Han) is also a very worthwhile goal, and I applaud the sketch of how this might be achieved here.That said -as an active researcher in the field of social ontology and having published extensively on the insights of Arendt and others encountered in this article -I think there are some problems with the essay.First, I think the contrast between indigenous knowledge and 'science' is presented in excessively sharp and generalizing terms.The image of science as dealing only with an ontology of objects is that of positivism.But positivism is no longer the dominant
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.023 |
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".