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«I dag, i fredstid, kreves det atter offervilje fra våre flygere»

2019· article· no· W2922940023 on OpenAlexaboutno aff
Sondre Brandsæter Hvam

Bibliographic record

VenueArbeiderhistorie · 2019
Typearticle
Languageno
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

På 1950-tallet forberedte Luftforsvaret seg på en krig som kunne komme når som helst. Øvelser og beredskap førte til en meget hektisk aktivitet ved de norske skvadronene. De norske militærflygerne var innforstått med at de når som helst risikerte å måtte ofre alt – hvilket mange av dem også gjorde. Fra 1950 til 1959 fant det sted 95 fatale flyulykker i det norske forsvaret, inkludert fly ført av nordmenn under utdanning i USA og Canada. Disse ulykkene kostet til sammen 116 militærflygere livet. Om vi ser på perioden vi betrakter som den kalde krigen under ett kommer vi opp i 161 fatale flyulykker, hvor tilsammen 199 militærflygere mistet livet. Samlet utgjør dette kanskje den største arbeidsulykken i moderne norsk historie. Likevel fremstår ulykkene som en lite kjent del av norsk forsvars- og arbeidshistorie. Gjennom en systematisk gjennomgang av Stavanger Aftenblads arkiv for perioden 1950–1989 viser artikkelen at de fatale militærflyulykkene fikk stor samtidig pressedekning for deretter å bli glemt. Til tross for at ulykkene enkeltvis skapte avisoverskrifter, gikk de raskt ut av folks bevissthet og har aldri blitt en del av vårt kollektive minne.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.122
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0930.032

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.

Opus teacher head0.016
GPT teacher head0.291
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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