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
Fast-Tracking to accelerate, overlap or compress schedules has an impact on project predictability in terms of achieving the planned objectives (time, cost, and quality). Predictability plays an important role in project success. Some studies focused on the fast-tracking impact on each objective; however, no research directly addressed the relationship between fast-tracking and predictability of the project's objectives. This paper investigates the relationship between fast-tracking and predictability with regard to success in meeting the project's planned objectives. A literature review was used. Significant findings in the study are the confirmations of the literature about the impact of fast-tracking on project predictability. This impact is that fast-tracking may lead to less predictability for the project's outcomes. The research results emphasize the need for further investigation of the relationship between fast-tracking technique and the project's predictability indices (cost variance, time variance and quality variance) in order to a achieve better understanding of the relationship and improve predictability.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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