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Record W2046233941 · doi:10.1017/s0020743812001316

Locating Afghan History

2013· article· en· W2046233941 on OpenAlexaboutno aff
Nile Green

Bibliographic record

VenueInternational Journal Middle East Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipAfghanFraming (construction)HistoryParallelsNarrativeQuarter (Canadian coin)NationalismSociologyPolitical scienceLiteraturePoliticsLawArtArchaeology

Abstract

fetched live from OpenAlex

Afghanistan's 20th century has long been seen through an analytical dichotomy. One concentration of historical scholarship has sought to explain the fraught progress of Afghan nation-building in the 1910s and 1920s. A second has sought to explain the unraveling of the Afghan nation after 1979. Weighted toward the decades at either end of the century, this dichotomized field has been problematic in both chronological (and thereby processual) and methodological terms. On the level of chronology, the missing long mid-section (indeed, half) of the century between the framing coups of 1929 and 1979 has made it difficult to convincingly join together the two bodies of scholarship. Not only has the missing middle further cemented the division of scholarly labor but it also has made it more difficult to connect the history of the last quarter of the century to that of the first quarter (except as a story of parallels), rendering them discrete narratives of development, one ending and the other beginning with a coup. The problems are deeper than this, though, extending from questions of chronology and process to matters of method. For if in its focus on nationalism and nation-building the first-quarter scholarship is framed within the neat boundaries of national spaces and actors, then in its focus on the unraveling of the nation and its peoples through the consequences of Soviet intervention, the last-quarter scholarship elevates nonnational actors as the key agents of historical process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.146
GPT teacher head0.345
Teacher spread0.199 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations17
Published2013
Admission routes1
Has abstractyes

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