Instrument for measuring the result (IMR): a standardized model for contract supervision and remuneration
Bibliographic record
Abstract
This master’s dissertation aimed to identify the obstacles to this end, a qualitative, applied and descriptive research was carried out with the managers and technical inspectors of contracts of the Federal Institutions of Higher Education (in portuguese the initials is IFES). The data were collected through interviews with contract managers and questionnaires with contract technicians in the first quarter of 2021. The sample consisted of 10 managers, 02 per region of the country and 94 technical tax officers. The answers of the interviews were analyzed with the help of the Iramuteq statistical software and the questionnaire data were tabulated in spreadsheets of the Microsoft Excel software. In this way, the main obstacles and difficulties encountered in the contract management and monitoring process have been identified and a measurement instrument (product) model has been proposed in order for it to become an effective tool in contract monitoring and that, through it, to provide greater efficiency in the monitoring and remuneration of service contracts in the public sphere.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".