Indigena Solutions, Tensions in an Aboriginal IT Impact Sourcing Firm
Bibliographic record
Abstract
This paper explores the role of cultural tensions in the operations of an Information Technology Impact Sourcing (ITIS) venture called Indigena Solutions. The company was based in Vancouver Canada and was intended to provide meaningful careers to a marginalized group, namely Canadian Indigenous Peoples. The company provided on-shore IT services such as help-desk support and software testing. With the support of Accenture and with initial success at high profile clients such as BC Hydro and Vancouver City Trust, the company lasted about seven years. Indigena was declared bankrupt in 2017. This research demonstrates the cultural tensions inherent in creating an Impact Sourcing venture, with challenges of location, the inability to attract Indigenous workers to a non-traditional career, and the underlying challenge of structural racism, despite the recognized social responsibility and commitment to help a marginalized group. The research was designed as a case study using an interpretive approach. The originality of this research rests on the exploration of why Impact Sourcing in a developed country failed to meet the needs of the marginalized Indigenous Peoples community. This research contributes to the body of work that explains tensions inherent in ITIS. Practitioners may find this research valuable as they consider the challenges of establishing and successfully operating an ITIS firm.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.044 | 0.018 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".