Bibliographic record
Abstract
Way back in the 1980s corporations began collecting, combining, and crunching data from sources through-out the enterprise. This approach was widely accepted as a methodology that provides objectivity and trans-parency in decision-making. Good processing of the garnered data paved way for improved analysis of trends and patterns leading to better business and increased profit margins. Corporations began investing in collect-ing, storing, processing and maintaining enterprise wide data. The focus was always on the quality of data and the process of converting it into knowledge that enables right decisions. It was soon realized that a wide range of personal biases has an impact on the way decisions are made. The entire process is replete with ethical dilemmas. This paper provides a framework to understand the interplay of data, information, personal biases, ethics and decision-making. This approach is suitable for every individ-ual, team, organization or a nation. Several years of turmoil in South Africa make it imminent for it to take a fresh look at the way data is transformed into knowledge. The leadership within South Africa has to arrive at wise decisions that can withstand the scrutiny of generations to come.
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 | 0.219 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.077 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".