Strategic Intelligence Summary Report
Bibliographic record
Abstract
Two keynote speakers, five plenery sessions, 29 concurrent sessions and over 75 presentations added up to three very full days at the Knowledege in Motion/08 international conference in St. John's, Newfoundland and Labrador, Canada, October 16th to 18th. During those days, over 200 conference delegates explored the many ways in which higher education institutions mobilize knowledge to affect regional development. Go to www.knowledgeinmotion.ca to see the confefence program, videos and presentarions. Conference organizers made every effort to ensure the event lived up to its name. The Strategic Intelligence Group was invited to apply its Strategic intelligence Process at the conference. The process helps turn information from a wide range of sources into individual and collective knowledge. For example, have you ever returned home from an excellent conference with only a vague sense of actual take-away? The information most relevant to you finds a home; the rest,even good information, slips away. The Strategic Intelligence Group process has been designed to provide Knowledge in Motion/08 participants with a shared take-away. The conference offered a wealth of information at keynote and plenary sessions. Using onsite polling of good questions, table discussion and a post-conference internet survey, the Strategic Intelligence Group "screened" the vast ammount of information through the unique experience and expertise of conference participants.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.257 | 0.179 |
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".