Bibliographic record
Abstract
Abstract The chapter describes an approach to teaching people how to understand organisations by focussing on observing what is going on in the group itself and the experiences of the members individually and collectively. This mode of learning does not use descriptions or theories about organisations but sees each group as unique in its particularities not as generalisable behaviour. The approach is called subjective theory and provides a basis for a general theory of organisations which has eluded most writers but is epitomised in the work of Carl Rogers and Encounter Groups. The method fits well with the concept of the reflective practitioner and has a long tradition dating from the 1930s and the work of the Tavistock Institute and Elliot Jaques, and the Glacier Papers. The approach has been poplar in the UK, Canada, the USA, Western Europe, South Africa and Australia. Subjective theory provides a way of counterbalancing the currently dominant objective approaches used in artificial intelligence which often becomes reductionist and simplistic.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.180 | 0.003 |
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; both teacher heads agree on what is shown here.
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".