Exploring trends in sourcing software development: A global and BC persepective
Bibliographic record
Abstract
Both internal and external sourcing have always been a mechanism for companies to augment their staff. Perhaps surprisingly, the approaches have similarities in their benefits and their challenges, especially when sourcing talent outside country. This project considers “to what extent does the BC Technology sector approach augmenting software development talent through sourcing and how does it align with current global trends?” This exploratory project looked first globally and then locally. A literature review provided insight into academic direction, theories and models of global outsourcing that have evolved during the last twenty-five years. The website review and industry related articles illuminated the current state of outsourcing and future trends. Finally, the project utilized a survey to explore trends within British Columbia; whether sourcing was being utilized and the models being used. As a result, new areas of study and opportunities for businesses to leverage sourcing have been exposed and discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".