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Record W3016507706 · doi:10.3968/11561

Quantitative Technique: A Missing Gap in Africa’s Contemporary Historiography

2020· article· en· W3016507706 on OpenAlexvenueno aff
Cyril Anaele

Bibliographic record

VenueCross-cultural communication · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican studies and sociopolitical issues
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyObjectivity (philosophy)Comparative historical researchNeglectMultidisciplinary approachNarrativeHistorical methodNorm (philosophy)Quantitative historyPresentation (obstetrics)Qualitative researchSocial scienceSociologyHistoryEpistemologyPolitical sciencePolitical historyPsychologyPoliticsLawArchaeologyLiteratureMedicine

Abstract

fetched live from OpenAlex

Historiography, as a technique of writing history is a combination of art and the sciences. The over – ridding objective is to present history with commitment to truth and objectivity. To achieve this, the African historians working on Africa rely on a number of sources. However, in their presentation, they have over – used the traditional conservative historical method, which is a combination of the narrative and unit analysis with focus on qualitative technique. Over the years, the qualitative approach has become the norm in African historical research. This trend still persist unbroken till today. The position of this study is that the over – reliance on qualitative approach to the utter neglect and apparent under – use of quantitative method has created a missing gap in historical research. This is because contemporary burning issues in Africa can no longer be adequately understood and addressed without the use of innovative multidisciplinary mix, integrating politico – economic social, institutional sectors with quantitative analysis, in order to achieve an evidence based research for the solution of Africa’s multifaceted challenges.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.819
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.172
GPT teacher head0.424
Teacher spread0.252 · 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 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

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