Smudging, connecting, and dual identities: case study of an aboriginal ERG
Bibliographic record
Abstract
Purpose Drawing upon the theoretical concept of social identities, the purpose of this paper is to investigate if an aboriginal employee resource group (ERG) helps to improve connectedness between the participants of the ERG and the organization in a Canadian context. Design/methodology/approach Qualitative research was used to interview 13 members of this ERG situated within a large Canadian bank. Findings The ERG created a positive experience for its members. It provided a bridge between the aboriginal identity and the organizational identity. Those who were part of the ERG felt that it encouraged them to bond to their cultural identity and that it also generated affirmative connections to the organization. Practical implications For employers seeking a more diverse workforce who have struggled with retaining employees from marginalized groups, ERGs may prove helpful. Originality/value This study posits a theoretical perspective of how ERGs are able to connect minority members to organizations through the recognition of dual identities. This is also the first study to examine the benefits of an aboriginal ERG.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".