MétaCan
Menu
Back to cohort
Record W3112636527 · doi:10.7202/1073850ar

Selling South Africa: Tourism and the Construction of a Post-Apartheid Nation

2020· article· en· W3112636527 on OpenAlexvenueno aff
Michelle Moore Apotsos

Bibliographic record

VenueMaterial Culture Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSouth African History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsTourismNegotiationPoliticsCapital (architecture)IdeologySpace (punctuation)Identity (music)Political economyPolitical scienceBuilt environmentEconomySociologyGeographyAestheticsLawArchaeologyCivil engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

South Africa’s nationalistic landscape is currently defined by a series of monumental, architectural edifices whose symbolic capital lies in their assumed ability to generate ideological unifications within the country’s charged, often fraught, spaces. Yet the failure of these forms to address the tensions and divisions that continue to define South Africa’s current socio-political climate raises questions as to whether such architectures are capable of acting in a nationalistic capacity within a country that is still negotiating its contemporary post-Apartheid identity. This paper suggests an alternative built landscape for consideration within discussions of South Africa’s current nation-building apparatus, specifically that of tourist space. The unregulated utilization (and exploitation) of structural languages from South Africa’s traumatic past allow tourist space to function as a “counter-monument” within the country’s contemporary nationalistic environment, provoking uncomfortable but potentially necessary confrontations with the charged elements of South Africa’s history as well as their continuity into the present period.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.007
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.002
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 head0.027
GPT teacher head0.242
Teacher spread0.215 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMaterial Culture ReviewSame topicSouth African History and CultureFrench-language works237,207