Indigenous Work Across the Employment Cycle: A Content Analysis of the Empirical Literature
Bibliographic record
Abstract
Abstract Indigenous employment is a crucial but often ignored area of economic and human development in the social sciences scholarship. Most disciplines that engage with Indigenous affairs (e.g., health, education) acknowledge symptoms of poverty and underemployment but fail to address underlying issues related to work. Disciplines responsible for employment research (e.g., organizational sciences) have historically served government (e.g., the military) or private corporations (i.e., human resources), limiting empirical translatability to Indigenous experiences. Further, the sparse publications on Indigenous employment available appear in journals that are only tangentially concerned with employment issues and not connected to dedicated Indigenous employment research programs. This article describes a quantitative content analysis on 215 empirical Indigenous employment studies. An inductive coding technique on manifest content derived 25 Indigenous employment-related constructs. The most frequently occurring constructs included Indigenous career development, (under)representation, cultural (mis)fit between employees and their organizations, and relationships at work. Statistical dimension reduction techniques identified two Indigenous employment experiences trending across the empirical findings: (1) Culturing work, and (2) Negative experiences at work. Culturing work describes how culture impacts participation in employment; for example, work within Indigenous communities, building relationships, and maneuvering work expectations while maintaining cultural identity, ethicality, and authenticity at work; while negative work experiences described experiences like overload, discrimination, mental health detriments, role conflict, being devalued, and organizational supports that stifle or facilitate positive work experiences. We discuss how these trends occur across the employment cycle with examples at each employment stage. Directions for future research and practice are presented throughout.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".