Bibliographic record
Abstract
LeadershipIn this issue we present the first article in a new IRRODL section, Leadership in Open and Distance Learning Notes.There is little doubt about the importance of leadership in all organizations in the complex and ever-changing context of the twenty-first century.The cult of leadership is especially visible to us now as the Americans, the French, and the Russians crank up their respective presidential leadership campaigns.But closer to home and the workplace, it is easy to think about leadership as being something for which someone else, higher up, is responsible.Surely our problems exist because the president, the provost, my department head, or my colleagues just aren't being effective leaders!However, crowd sourcing, the viral influence of individual blogs and YouTube posts, and the power of tweets and Facebook posts force all of us to confront and take responsibility for our own leadership capacities.We can exercise a great deal of leadership in our homes, schools, and workplaces, but that leadership demands commitment, energy, and risk.All of us, as distance education researchers and practitioners, are challenged to maximize and optimize our respective leadership contributions.Our collective mission, to expand opportunity and to increase the development and effective use of knowledge, demands that we be leaders and develop our individual leadership capacities.We hope this new series will help all of us to become more effective leaders.We welcome new articles from both students and practitioners for this series.The Leadership in Open and Distance Learning Notes section is edited by Professor Marti Cleveland-Innes from the Centre for
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.055 |
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".