Developing industry-wide information management capabilities: A case\n study from British Columbia's tree fruit industry
Bibliographic record
Abstract
As an industry of primarily small and mid-size businesses, it is becoming\nincreasingly more difficult for British Columbia (BC)'s tree fruit growers to\ncompete with large, often vertically integrated producers from other regions.\nNew ways of managing information and resources collaboratively are needed to\ndevelop competitive strengths. This case study seeks to understand the\ninformation and knowledge management capabilities of the BC tree fruit cluster\nacross the value chain for six different data domains. A qualitative\nmethodology design of 21 in-depth interviews with cluster stakeholders provides\ninsights into the data quality, completeness and integration points, and then\napplies CMMI level criteria to assess the information management capabilities\nof the industry. Significant data and process gaps are identified. This paper\nexplores how the BC tree fruit industry can move forward from this position\nusing technology solutions to support the development of information and\nknowledge, and collective decision-making.\n
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".