Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To overcome the disadvantages of the low bid price policy in open competitive contracts, many are advocating the average bid method for bid evaluation. However, as elucidated in this study, this method has some disadvantages. The purpose of this paper is to propose an alternate statistical procedure for bid evaluation. Such procedure applies simple statistical analysis to identify unrealistically low-priced bids, based on either the t-distribution or the normal distribution of a previously established database of similar bids. In this procedure, the ratio of a contractor's bid to owner cost estimate is used to eliminate the distorting effect of the project size. The unrealistically low-priced contracts are then excluded and the bid with the lowest price among the remaining bids is accepted. The procedure requires establishing a database of previous bids.Key words: bid evaluation, competitive bidding, contractor qualification, tender evaluation, contract administration, contract management, bid management.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it