Dark Side Case: Rogue One: The Canadian Space Agency and “Understanding the [Non] Inclusive Organization”
Bibliographic record
Abstract
Samaa, a woman/Muslim/lesbian/engineer/expatriate, is the protagonist that the students follow throughout this Dark Side Case. She is a highly technically-trained individual, who is in her early career at the Canadian Space Agency (CSA). She faces, on a day-to- day basis, mostly all White, heterosexual men who are technically- trained and who reflect the organization’s approach to inclusivety, or lack thereof, with respect to her complexity as an individual. She also sees evidence that some of her women colleagues, mostly White, who self-identify as being part of the non-dominant class and who have been in the industry longer than she has, are facing their own challenges within the CSA. Samaa is exploring how to confront the twin challenges of her day-to-day experiences while also aknowledging that the organization needs to evolve from merely complying with Canadian Employment Equity (EE) legislation into valuing the complex individual and inclusivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.082 | 0.043 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".