Bibliographic record
Abstract
Governments across the globe appear to identify and tout technology as a way to transform how they govern. Public procurement is at the forefront of most reform efforts given that it plays a significant role in promoting accountability and transparency. This study relies on survey data of procurement professionals to delineate the current status of eprocurement implementation in United States and Canada. Findings suggest that digitalized public procurement has not yet led to significant transformative changes. Unsuitability of software platforms, organizational resistance, lack of strategic systemsʼ integration and failure to involve public procurement professionals in the design of e-procurement systems were identified as the primary obstacles of effectively implementing digital procurement. These findings suggest that in order to capitalize on the potentially transformative nature of ICT in procurement, policymakers, system designers, and procurement professionals must take an active role in both the design of the software and its adoption across political, institutional and behavioral domains.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.017 | 0.034 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".