The efficacy of gender-based federal procurement policies in the United States
Bibliographic record
Abstract
Purpose Because procurement policies are one of the means of redressing discrimination and economic exclusion, the US Government has targeted 23 per cent of its annual half-trillion dollar spend to small- and medium-sized enterprises (SMEs) and 5 per cent of its spend to women-owned businesses. Design/methodology/approach The research framework is informed by two theoretical paradigms, feminist empiricism and entrepreneurial feminism, and uses a secondary analysis of survey data of active federal contractors. Findings Empirical findings inform the extent to which certifications are associated with bid frequency and bid success. The results indicate that none of the various certifications increase either bid frequency or bid success. The findings are consistent with entrepreneurial feminism and call for federal accountability in contracting with women-owned supplier firms. Research limitations/implications The findings are consistent with entrepreneurial feminism and call for federal accountability in contracting with women-owned supplier firms. Practical implications Recommendations include the need to review the impact of consolidated tenders on designated (as certified) SME vendors and to train procurement personnel about the economic contributions of women-owned businesses. Originality/value This research studies the efficacy of various certifications, with particular reference to that of women-owned, on the frequency with which SMEs bid on, and succeed in obtaining, US federal procurement contracts.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".