Bibliographic record
Abstract
On 2 January 2018, V. K. Benugopal reviewed the company’s performance for the previous quarter. He thought about the challenges he had faced during the quarter and was convinced that time was running out to make things right. Benugopal took charge as chief executive officer (CEO) of Panth Transfreight Limited (PTL) in July 2017 and was entrusted with the responsibility of streamlining logistics operations for Indian Steel and Power Ltd (ISPL). He was also tasked with eliciting a cost savings of 10% from the existing logistics costs. From the very beginning, Benugopal made critical changes in how logistics operations were managed at ISPL. He centralized operations and contracts, and changed the freight-finalization process from destination-wise freight to a region/state-wise freight concept. He created an information-technology portal to allow for a digitized billing process, and implemented a vehicle-tracking mechanism. These changes created unrest among employees at the plants and confusion among transporters, which resulted in declining operational performance. Benugopal was anxious as he considered his circumstances. Did he do the right thing by implementing so many changes at one time, or should he have made these changes gradually and checked system readiness?
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.101 | 0.024 |
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".