Bibliographic record
Abstract
Attending the Learning Conference 2004 in Havana, Cuba was a unique experience.It allowed delegates to explore the challenging learning themes of communication and technology, all within an environment that was itself pedagogical.My one regret about this otherwise splendid event was the necessity of concurrent sessions that made hearing all the papers impossible.Amongst the wonderful variety of papers presented, I would like to mention two in particular.First, Navigating Unchartered Waters: Peace within Hearts, Hands and Minds presented by Sheila Devkaran Narsee, a senior lecturer in the Department of English and Communication at the Durban Institute of Technology in South Africa, captured everyone's attention.Narsee addressed the "need to foster worldwide institutional collaboration and to reconsider conceptualizations of teaching, learning and researching education, encompassing a concern for human rights and a culture of peace."Education is a fundamental element in all processes that aim to build a culture of peace and human rights.The notion of diversity as is currently applied in South Africa is clearly one that includes race, culture, language, gender, class, and ability, however, these issues must be given far more attention and importance than they are currently.Narsee's paper is influenced by the underlying principle of Mahatma Gandhi's philosophy of nonviolence.She argues that given the circumstances of today's society, education must be rethought in order to achieve a balance between academic
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.189 | 0.044 |
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".