Bibliographic record
Abstract
In reverse logistics (RL), the returned products are collected and some recovery activities are applied. The objective of this project is to identify the RL components of cellphones in three provinces of Canada. There have been a number of investigations performed in this sector. But in recent years with the technological advancement of cellphones along with the evolution of smart phones, a rapidly growing secondary market has developed and the RL of cellphones has been very complex. In this project, we make the effort to draw a clear picture of the RL framework of cellphones in Canada, highlighting the policies and practices in three provinces: British Columbia in the west coast, Ontario in the middle, and Nova Scotia in the east coast. In doing this, we discuss about the physical, chemical and recoverable components of cellphones as well as the recovery options. We review the literature on cellphone RL, identify some relevance as well as differences comparing with the Canadian context and address some issues pertinent to provincial contexts. We focus on the high paced growth of the cellphone secondary market and the need for more research on the same. Finally, some managerial insights and suggestions on how the cellphone RL can be more efficiently handled are offered.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".