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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".