Effects of Newcomer Practicing on Cross-level Learning Distortions
Bibliographic record
Abstract
This article fuses variance generation and suppression arguments with the micro-underpinnings of collective learning to bring the socio-emotional context of learning to the foreground. We take a practice-based perspective on cross-level learning distortions to explore non-recursive trade-offs between variance generation and variance suppression as newcomers adapt to established groups and as groups react to newcomers. Our typology first disaggregates the effects of sociality and emotionality to describe four patterns of context-contingent individual practicing: experimenting, emulating, bracketing and impersonating. We then explain why groups operating in distinct contexts may systematically ignore or discount two specific types of individual departures from collective norms: outliers (infrequent, significant deviations) and clusters (frequent, incremental changes). Our theoretical predictions add value to managers by unpacking the contextual contingencies that systematically pattern individual and collective learning and by suggesting specific interventions for preventing or alleviating learning disorders.
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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 | 0.007 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".