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Record W2305591539 · doi:10.1177/0308275x15617303

Sweeping as a site of temporal brokerage: Linking town and forest in Mozambique

2015· article· en· W2305591539 on OpenAlexfundno aff
Ingrid L. Nelson

Bibliographic record

VenueCritique of Anthropology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsnot available
FundersMcGill UniversitySyracuse University
KeywordsTemporalitiesWoodlandUndergrowthEthnographyLoggingSpace (punctuation)GeographySociologyEnvironmental planningPolitical scienceEconomic growthForestryEcologyArchaeology

Abstract

fetched live from OpenAlex

Building on 18 months of ethnographic research between 2007 and 2011 in Mozambique, this article explores how sweeping practices elaborate multiple temporalities and thus can serve as powerful sites of “brokerage” where diverse actors and their many agendas meet. Sweeping renders daily activities legible to local residents and illegible to others. Daily sweeping just outside of the home signifies a day’s routine beginning and a family’s availability for receiving visitors. Sweeping also prevents the rapidly growing miombo woodland undergrowth from invading the home space, which partly explains differences in why many local residents see the woodlands as advancing and environmentalists’ argue that the “forest” is disappearing due to illegal logging. Limited understandings of sweeping as banal or wasted time miss the links between many urban-based non-governmental organization and activist practitioners who implement their projects in rural areas. Sweeping blurs and challenges assumed boundaries between urban and rural woodland spaces.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0110.011
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.377
Teacher spread0.335 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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