Thought leadership on intellectual capital
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
A synopsis of key topics, issues and findings as presented at the 4th World Congress on Intellectual Capital hosted by McMaster University in Hamilton, Ontario, Canada. There were 536 delegates from 32 countries discussing the growing importance of intellectual capital. This paper highlights the key messages from the keynote speakers of the conference. Includes a summary of the presentations of such luminaries as Shahla Aly, VP Communication Sector at IBM Canada; Stephen Denning, KM director of the World Bank; Don Tapscott, chairman of Digital 4Sight; Ante Pulic and Ursula Schneider, directors of the Austrian Intellectual Capital Research Centre; Tom Jenkins, CEO of OpenText; Leif Edvinsson, VP of Knexa.com Enterprises; Verna Allee, president of Integral Performance Group; and Don Morrison, COO of Research in Motion.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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