Drivers of Coopetition in the Plastic and Composites Material Industry
Bibliographic record
Abstract
In this research, we explore which activities may be subject to coopetition in the plastic and composites industry. We also compared the main antecedents, outcomes, and moderators of coopetition in the plastic and composites industry with those identified in existing literature. Results indicate that the respondents have a desire for coopetition, but for activities not close to the customer (sales, after-sales service, customer information). On the other hand, respondents are in favor of collaborations for sharing (1) costs of shipping and/or importing raw materials, (2) information on other competitors, (3) technical expertise on non-exclusive products, and (4) information that could have an impact on the partner. In terms of antecedents, we found that there are positive elements that favor the creation of coopetition. However, certain elements at the relational level obstruct the formation of coopetition, such as (1) reciprocity, (2) fairness, (3) integrity, and (4) keeping promises.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 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".