Bibliographic record
Abstract
If the last issue of JISIB was a special issue where the discipline was reflecting on itself, then this issues shows some of the width and scope of the field. The conceptual article by Nienaber and Sewdass presents a relatively new concept of workforce intelligence, and links it to competitive advantage by way of predictive analytics. The article by Solberg Søilen is an attempt to lay out a broad scientific agenda for the area of intelligence studies in business.Empirical findings come from a survey, but in the discussion the author argues for why the study should define itself as much broader than what the survey data implies, breaking out of the current dominating scientific paradigm. The article by Fourati-Jamoussi and Niamba is an updated evaluation of business intelligence tools, a frequently reoccurring topic. However, this time it is not a simple evaluation of existing software, but an evaluation by users to helpdesigners of business intelligence tools get the best efficiency out of a monitoring process. The article by Calof is an evaluation of government sponsored competitive intelligence for regional and sectoral economic development in Canada. The article concludes that it is possible tocalculate positive economic impacts from these activities. Rodríguez Salvador and Hernandez de Menéndez come back to a field that has become a specialty for Rodríguez Salvador: scientific and industrial intelligence based on scientometric patent analysis. This time she looks at bio-additive manufacturing using advanced data mining software and interviews with experts.
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 | 0.148 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.044 | 0.056 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".