Capabilities and collaborative marketing practices among rival cluster-based wine producers
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
This study investigates how owner-managers of wine producers that participate in coopetition relationships (cooperation with competitors) can effectively implement collaborative marketing practices with their cluster-based rivals to facilitate regional sales as a specific place-related strategy. Data collection featured interviews with owner-managers of smaller-sized, independent, family-owned, wine producers in New Zealand, alongside secondary data. Contributions are offered first, by profiling firms via a 2 × 2matrix, regarding decision-makers’ capabilities associated with gaining either a high/low recognition of types of customers’ knowledge and purchase intentions, also, by exhibiting a high/low competitor orientation. Second, unique insights emerge concerning the capability to be strategically flexible in response to environmental conditions. Third, new evidence indicates that capabilities vary, but some are of a lower-order ‘threshold’ rather than higher-order ‘dynamic’ nature.
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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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