Bibliographic record
Abstract
Abstract: Educational conceptual frameworks and vocabulary need to evolve with modern, societal expectations. Education curriculum developers should use the term humanagogy as the appropriate 21st century replacement for the antiquated terms pedagogy and andragogy. Humanagogy has no inherent age and gender references, which, in an increasingly inclusive society, makes it superior to the former terms. Before I encountered that new word, I perceived andragogy as most the appropriate descriptor when referencing adult education; but, for reasons unknown, it was not being used broadly and consistently. In the process of strengthening the case to expand the use of andragogy, I discovered several “gogys,” including teliagogy, metagogy and humanagogy. Further investigation and analysis led me to determine that humanagogy was the best option. Surprisingly, the term is not new; it was introduced approximately 40 years ago. The purpose of this paper is to raise the profile of humanagogy and to encourage a broader adoption of its use among educators, as a symbol of a more enlightened era. Furthermore, a change in vocabulary will likely create further opportunity for the evolution of associated conceptual frameworks.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".