Bibliographic record
Abstract
This paper provides effective solutions for removing silos between commercial teams in order to improve in-market performance. The proposed steps are: (1) Single-sourcing the reporting commercial structure into the marketing officer or commercial officer; this consolidates the commercial responsibilities within the organisation while establishing a common set of performance metrics across the different commercial teams. (2) Shifting focus from ‘path to purchase’ to ‘path to market’; this addresses the incomplete knowledge about the path the product takes from the vendor to the customer. (3) ‘Sense–Make Sense–Respond’; this step demonstrates the need for a common construct that allows the organisation to access information quickly, understand that information, and take actions to maximise its performance in the marketplace. (4) Disrupting inertia; this final step requires the change agent to be patient with company and/or individual willingness to disrupt the inertia inherent in most mature organisations. Having awareness about what it takes to remove silos yet choosing to do nothing will only cause a company to underperform in the marketplace. Ignorance is only bliss when it is truly a lack of knowledge — but this will never lead to improvements within the organisation.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".