Bibliographic record
Abstract
The existing research on building workplace inclusion has been mostly focused on eliminating biases and micro-aggressions that reduce it. This approach is important, but far from enough. We propose a micro-affiliation theory, where small gestures can promote inclusion. The theory further identifies two dimensions – group-directed/individual-directed, appreciating difference/recognizing similarity and four kinds of micro-affiliation – micro-celebration, micro-normalization, micro-socializing, and micro-affirmation. We explain why intervention that is designed based on micro-affiliation is a more effective approach than others and propose some conditions under which each kind of micro-affiliation can best exert its positive influence. The remainder of the paper focuses on the implications of micro-affiliation, including its potential of changing workplace acculturation, and impressions of actors and recipients. Micro-affiliation also has the capacity to increase employee retention, maintain and strengthen diversity among emerging leaders, promote organizational citizenship behaviours in the workplace, and create better work-family balance. Our approach complements those focused on eliminating biases and micro-aggressions – behaviors arguably that reduce inclusion – by focusing on those that increase it. This model points to new directions on where the literature on inclusiveness – arguably one of the most defining topics of the social sciences – can go for improving organizational and societal diversity and effectiveness.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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; 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".