Indigena: Teaching case of IT Impact Sourcing
Bibliographic record
Abstract
This teaching case introduces the concept of Impact Sourcing, in the context of global IT outsourcing. While IT outsourcing is a well-established management technique, with a history of at least 30 years, Impact Sourcing is a relatively new concept, conceptualized by the Rockefeller Foundation in 2011 and recently defined by the Global Impact Sourcing Coalition. To summarize this case, a First Nations band collaborated with a successful global outsourcing firm, Accenture, to establish Indigena, a Canadian-based impact-sourcing enterprise. Indigena found it difficult to attract, hire and retain qualified Aboriginal employees. The suburban office location in the high-cost Vancouver market may have been a key challenge in building a robust Aboriginal workforce. However, the challenge of winning outsourcing contracts in a competitive market may have been hindered by the Aboriginal workforce, despite the outward positive response of clients to the Impact Sourcing model. Indigena was not able to meet its social goals and at the same time it struggled to attract and retain clients. It was unable to demonstrate profitable business success, resulting in a strategic challenge from its investors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".