Bibliographic record
Abstract
municators’. The needles, targets, and audiences of communication and development models, combined with self-righteousness, titles, and insecurities, perhaps sprinkled with a dash of misdirected benevolence, often render ‘experts’ a bit too verbose and pushy. Perhaps this is because it requires much more imagination, preparation and hard work to have dialogical learning. It is far easier to prepare and give lectures. However, there is possibly a valid reason why we have two ears, but only one mouth. Communication between people thrives not on the ability to talk fast, but the ability to listen well. People are ‘voiceless’ not because they have nothing to say, but because nobody cares to listen to them. Authentic listening fosters trust much more than incessant talking. Participation, which necessitates listening, and moreover, trust, will help reduce the social distance between communicators and receivers, between teachers and learners, between leaders and followers as well as facilitate a more equitable exchange of ideas, knowledge and experiences. However, the need to listen is not limited to those at the receiving end. It must involve the governments as well as the citizens, the poor as well as the rich, the planners and administrators as well as their targets. In this chapter we present:
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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.018 | 0.041 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".