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Record W2776509282 · doi:10.29173/comp40

Non-modern Africa and modern non-Africa: Intertextual discourse about who we are and where we came from

2017· article· en· W2776509282 on OpenAlexaffvenue
Mat J. Levitt

Bibliographic record

VenueCOMPASS · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHumanityMeaning (existential)PoliticsSociologyPhraseIdentity (music)WishIsolation (microbiology)LinguisticsAestheticsEpistemologyHistoryMedia studiesAnthropologyPhilosophyPolitical science

Abstract

fetched live from OpenAlex

The idiom no man is an island might be rewritten as no text is an island, meaning that no text exists in literary isolation and that every piece of writing instead belongs to a landscape of others. References, endnotes, footnotes, bibliographies, and the use of phrase, term, or defi nition will situate a text within a discursive network of understandings, theories, paradigms, and genres. Intertextual discourses about the origins of humanity often cross generic boundaries between religion, science, informative media and entertaining fi ction through the sharing and borrowing of words and concepts. These genres, however reluctantly, inevitably engage with one another in what emerges as a single discourse about who we are and where we came from. Throughout this discourse, certain concepts have formed as time, place, and identity are linguistically associated with one another. The dichotomy of Non-Modern Africa as Other and ModernNon-Africa as Self is one such concept. Caution must be exercised by researchers who wish to talk about human origins, as these types of concepts may have very real implications in political, economic, social, and cultural arenas.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.999

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 studies0.0030.002
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 head0.050
GPT teacher head0.328
Teacher spread0.278 · 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
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

Citations1
Published2017
Admission routes2
Has abstractyes

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