Bibliographic record
Abstract
A confluence of kind-hearted people (in the right places), inspired to expand on a simple request from an influential old man, has so far enabled more than 500 underprivileged infants with serious facial deformities to smile properly for the first time. Not only have maxillo-facial surgeons across the country benefited from learning advanced surgical techniques, but top sporting folk and other celebrities have also seen the near-miraculous transformation of these little children’s faces -- and therefore their futures. The latest 27 tiny beneficiaries were last November given restorative surgery for a wide variety of facial abnormalities at Tygerberg Hospital near Cape Town during Smile Week, now a 6-year-old phenomenon in academic hospitals country-wide. The Smile Foundation was born of an idea that flowed from a simple request by former President Nelson Mandela to Gauteng businessman and philanthropist Marc Lubner. Madiba asked Lubner if he could facilitate surgery for an impoverished child he’d met who had a rare facial paralysis that prevented him from smiling. Lubner secured the services of top Canadian surgeons who worked with their local counterparts and realised ‘the synergistic power that a co-ordinated set of surgical programmes could have on a large number of children who could otherwise not afford this’. Next came health care philanthropist, pharmacist, and former Miss South Africa (1997), Kerishnee Naiker -- who happens to sit on the board of the Vodacom Foundation (the social responsibility arm of the telecommunications giant) and the Smile Foundation. During a 2007 Vodacom Foundation board meeting she strongly punted the Smile Foundation’s work, telling members it was ‘the kind of thing that needs showing not telling’.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".