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Record W3044690967 · doi:10.1177/0308275x20941573

Concrete violence, indifference and future-making in Mozambique

2020· article· en· W3044690967 on OpenAlexaff
Julie Archambault

Bibliographic record

VenueCritique of Anthropology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsConcordia University
FundersLeverhulme Trust
KeywordsProsperityPoliticsContext (archaeology)AfterlifeSilenceSociologyAestheticsPoeticsFunctional illiteracyHistoryPolitical sciencePoetryArtLawArchaeologyLiterature

Abstract

fetched live from OpenAlex

In the Mozambican suburb of Inhapossa, piles of fresh concrete blocks vividly convey a sense of the momentous transformation under way in a place where building is now described as being ‘in fashion’. Exuding promises of a better future, this fresh concrete is emerging amidst the ruins of a not so distant violent past, in a country where the built environment has been scarred by decades of war, economic decline, neglect and vegetalization. If ruins are reminders of what once was or of what could have been, what do they become in a context of growing prosperity? The contrast between fresh and rotting concrete seemed to beg for anthropological attention, to call for an approach that would simultaneously capture the poetics and politics of concrete throughout its life and even longer afterlife. What my ethnography revealed, however, was that unlike the fresh concrete, which inspired songs and made people fall in love, the decaying concrete scattered across the suburb often inspired little more than indifference. By examining how Mozambicans remember the past and project themselves into the future through their engagement with the built environment, I propose to approach indifference neither as refusal to engage, nor simply as silence, and certainly not as political illiteracy, but rather as an affective experience in its own right that speaks of a particular orientation towards the future.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.324
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations13
Published2020
Admission routes1
Has abstractyes

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