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Record W4206309714 · doi:10.26686/wgtn.17003404.v1

Auto-Ethnography in a Kabyle Landscape

2012· dissertation· en· W4206309714 on OpenAlexaboutno aff
Si Belkacem Taieb

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEthnographyColonialismNarrativeGender studiesSociologyGeographyEthnologyAnthropologyArchaeologyEcologyBiologyArtLiterature

Abstract

fetched live from OpenAlex

In this auto-ethnography, as an indigenous man in a Kabyle landscape, I take into account the relational experience that involves the development of a Kabyle identity. The indigenous cultures in North of Africa all come from the same family called the Imazighen (free men). Kabyle live in the North East of Algeria but there are other Imazighen living in the diaspora all over North Africa, from Morocco to Egypt, like Touaregs or Mozabites. My inquiry narrates my personal experience as a Kabyle man born of Kabyle parents in France. In this auto-ethnography I return to my father’s village to understand and access my heritage. I hope that this narrative helps my readers to reflect on the effects of globalization on the transmission of indigenous cultures. I portray Algeria, a North African Muslim country in 2010. I draw on critical pedagogy, socio-constructivism and indigenous knowledge and experiences. Looking to Algeria with the perspective of an indigenous person, I explore the social organization in my village and the way values and relationship shape the traditional education of a Kabyle man. My experiences and research in my ancestral village show that the war Kabyle people have fought against France has not resulted in independence. Rather, in my case, decolonization made me twice stranger to myself as Kabyle in an Arabic dominated country but also as an immigrant in France, the old colonial country, and Canada. However, my spiritual and sacred heritage is still alive in me, shaped by both my own experiences and the teachings of other members of my culture, and I have expressed this heritage throughout this narrative.

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)0.002
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.006
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.093
GPT teacher head0.410
Teacher spread0.316 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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