Will fleet managers really help vehicle fleets to become electric?
Bibliographic record
Abstract
Over the last few years, obligations to reduce carbon dioxide emissions have led European States to propose ambitious targets concerning electrifying car fleets. In France for instance, electric vehicles are required to cover a quarter of all new car purchases in big companies and public administrations. In these organizations, departments that are traditionally in charge of company vehicles have thus been tasked to implement these policy decisions. General Resources have become de facto responsible for testing and managing these new EVs. Illustrating our results through five case-studies that took place in France in 2012-2015, we will show how these departments, and notably fleet managers, carry out the numerous tasks accompanying the spreading of EVs in their organizations: acquiring these vehicles (and the charging infrastructure), allocating them and managing the charging of the cars. The allocation, whether as fleet cars or executive ones, is an important step for the success of their implementation in these companies. We will also point out the contradictory significations and powerful constraints that complicate the performance of these tasks. Their achievement strengthens the role of the fleet managers, who turn out to be crucial but unexpected players in electricity demand.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".