Bibliographic record
Abstract
In the 1958 musical South Pacific, the character Nelly Forbush trills a song of optimism and hope amid the darkness of World War II (South Pacific Enterprises and Logan 1958). The chipper message of this fictional navy nurse might well be welcome amid the negative timbre of the pervasive political, cultural and societal upheaval that we are experiencing today - not to mention the burden of a global pandemic. The tune delivers the message of a so-called "cockeyed optimist," staying positive while many are not and being buoyed by the anticipation of brighter, sunny days ahead (South Pacific Enterprises and Logan 1958). COVID-19 has unloaded countless blows to virtually every aspect of the life we once knew; surely, this is enough to leave any cockeyed optimist reeling. Where do we find the strength of character to prevail during times like this? Somehow, good leaders do; finding creativity, courage and conviction to make the most of a bad situation, they rise above it. They show optimism in the face of fear, the unknown and circumstances beyond their control. Instilling abiding trust in their followers, they lead out of the abyss, shining light on new possibilities and opportunities.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".