Bibliographic record
Abstract
tive and educational research has shown that post-lecture knowledge retention is minimal. Therefore, dental education must build upon lessons learned in the past and our approach to education must not simply mature, but change. Historically, students were accepted into dental programs after acquiring some comfort with the basic life sciences. Thus, in most cases, they arrived at dental school with a narrow educational background, particularly lacking an understanding of the social sciences and often deficient in interpersonal skills. Students spent the first 2 years of study concentrating on oral health sciences, while the final 2 years emphasized the application of what they had learned and the acquisition of clinical skills. It was assumed that by graduation all of these elements would come together, resulting in a competent practitioner. While this was often the case, many graduates left with the sense that the “science” and “art” of dentistry were clearly separable entities. Now we have arrived at a point where what we learned as students is constantly being questioned; old concepts are discarded and new theories proposed. The exponential growth of the body of knowledge makes it next to impossible for our graduates to have all the information they require neatly stored in their heads in order to provide appropriate oral health care to the public. The new dentist must have an enquiring mind and be a critical thinker. Rather than being the sole repository of knowledge, he or she must have access to a variety of credible information sources and know how to use them. It is no longer adequate to say “It works in my hands.” Wherever possible, the justification for new ideas, procedures or materials must be defined by the science that supports them. The phrase “lifelong learner” tends to i n n o v a t i o n s a t t h E f a c u lt y
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".