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Record W2089988178 · doi:10.1080/02582473.2010.493001

‘Route Work’ through Alternative Archives: Reflections on Cross-Disciplinary Practice

2010· article· en· W2089988178 on OpenAlexaff
Ann B. Stahl

Bibliographic record

VenueSouth African Historical Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDisciplineValue (mathematics)SociologyWork (physics)Quality (philosophy)Historical methodComparative historical researchEpistemologyEngineering ethicsAestheticsHistorySocial scienceArchaeologyEngineeringArtComputer science

Abstract

fetched live from OpenAlex

The Five Hundred Year Initiative stresses the value of cross-disciplinary perspectives in investigating the antecedents of contemporary southern African societies, with archaeological sources playing a prominent role in its associated projects. To produce effective histories, these projects require analytical attention to both the processes that gave rise to contemporary societies and those that shape history making in the present. In this article, I build on Paul Gilroy's notion of ‘route work’ (by comparison to ‘root work’) to explore the value and methodological challenges of constructing historical understandings that simultaneously attend to the dynamics of past cultural practice and the processes through which our understandings of those practices are produced. Drawing on examples from Ghana, I argue that a methodological engagement with the negotiated quality of historical understanding – an approach that brings into view our processes of ‘coming to know’ – lays the groundwork for an ethically engaged and responsive, at the same time as empirically grounded, understanding of past socio-historical processes.

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)0.026
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0260.039
Scholarly communication0.0140.014
Open science0.0030.017
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0100.001

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 head0.099
GPT teacher head0.416
Teacher spread0.318 · 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 designQualitative
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

Citations11
Published2010
Admission routes1
Has abstractyes

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