Autopraxeography: a method to step back from vulnerability
Bibliographic record
Abstract
Purpose Studies on vulnerability in the workplace, although relevant, are rare because it is difficult to access. This article aims to focus on the benefits of using autopraxeography to study and step back from vulnerability at work. Design/methodology/approach Autopraxeography uses researchers' experience to build knowledge. Findings Autopraxeography provides a better understanding of vulnerability and the opportunity to step back from the difficulties experienced. Instead of ignoring experiences related to vulnerability, this method makes it possible to transform them into new avenues of knowledge. Moreover, it enables researchers to step back from experiences of vulnerability, thus making them feel more secure. Originality/value The main differences from other self-studies stem from the epistemological paradigm in which this method is anchored: pragmatic constructivism. The most important difference is the production of generic knowledge in three recursive steps: writing in a naïve way, developing the epistemic work and building generic knowledge.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".