Bibliographic record
Abstract
Attention to women’s low participation in information technology is framed in Canada and elsewhere in terms of concern over availability of well-qualified human resources (ITAC & IDC, 2002) as well as equity issues (Applewhite, 2002; Ramsey & McCorduck, 2005). In most of these discussions, IT Professional is equated with Computer Scientist or Engineer in spite of the evidence that the profession is more diverse. This article suggests that while those directions are worthwhile, the very definition of “information technology professional” framed in the discourse may have unintended consequences which tend to exclude women. Framed by the literatures on gender and institutionalization of professions, this article applies critical discourse analysis to a variety of “texts” concerning the IT profession in Canada as well as available empirical data. Critical discourse analysis focuses on surfacing the political structures which underlie taken for granted assumptions (Fairclough, 1995). We maintain that while it is critically important to continue to attract females to study computer science and engineering, it is equally important to ensure that multiple paths are available and respected and that narrow definitions are not systemic barriers to the participation of women in the IT profession. In addition, more inclusive definitions which broaden the perspective on information technology (and match the reality of the industry) will promote good technology practices.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".