We Have Sent Ourselves to Iceland (With Apologies to Iceland): Changing the Academy From Internally-Driven to Externally Partnered
Bibliographic record
Abstract
In Brave New World, Aldus Huxley presented a dystopic vision of the world where global despotic power was maintained, in part, through isolating academics in Iceland. Current academic accountability is based on notions of excellence that reflect prestige. In governing itself based on excellence, I argue academia has metaphorically sent itself to Iceland, which has consequences for the relevance of academia toward sustainable development. Internally-driven academies are facing their own sustainability issues, as more students are trained for too-few professor positions, and must find work in other fields with inadequate training. Academic measures of excellence attempt to reflect merit but perpetuate pre-conceived notions of prestige, which is discriminatory, contributes to intellectual gate-keeping, and distracts from research rigor and policy relevance. Measures of excellence fail to translate to real-world impact in three important ways: academic reviews that accounts for prestige lead to poor and biased predictions of outcomes of research projects; prestigious individuals are not more reliable experts than less prestigious individuals (and may be more overconfident); prestigious institutions are not more likely to contribute to sustainable development outcomes than less prestigious institutions. It is time to drop academic notions of excellence and turn toward external partnerships, where academic institutions can focus more on real-world impact, train students for diverse careers, and allow academic research to focus on quality over quantity. For academia to be relevant to society, and to serve people graduating academic institutions, academia must proactively leave Iceland and rejoin the rest of the world.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".