Bibliographic record
Abstract
Abraham son , M. (1973) Con tractual risks in tun n ellin g: h ow th ey sh ould be sh ared.Tu n n els an d Tu n n ellin g, N ovem ber, 587-598.Ackerm an n , F., Eden , C., William s, T.M. an d H owick, S. ( 2007) System ic Risk Assessm en t: a case study.Jou rn al of the Operation al Research Society, 58(1), 39-51.Adam s, D .(1979) T he Hitchhik er's Gu ide to the Galaxy.Lon don : Pan Books.Adam s, D .(1980) T he Restau ran t at the En d of the U n iverse.Lon don : Pan Books.Adam s, J.R. an d Barn dt, S.E.(1988) Behavioral im plication s of th e project life cycle, Ch apter 10 in Clelan d, D .I. an d Kin g, W.R. (eds), Project Man agem en t Han dbook .Secon d edition .N ew York: Von N ostran d Rein hold.AIRMIC/ALARM/IRM (2002) A Risk Man agem en t Stan dard.Lon don : Association of In suran ce an d Risk Man agers (AIRMIC), Association of Local Auth ority Risk Man agers (ALARM), In stitute of Risk Man agers (IRM).AIRMIC In tegrated Risk Man agem en t Special In terest Group (1999) A gu ide to in tegrated risk m anagem en t.Lon don : T he Association of In suran ce an d Risk Man agers in Com m erce.Akerlof, G.A. (1970) T he m arket for 'lem on s': quality un certain ty an d th e m arket m ech an ism .
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.380 | 0.214 |
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".