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Cultural nationalism, interdisciplinary methodological proposals

2014· article· es· W4253612256 on OpenAlexaboutno aff
Pablo Giori

Bibliographic record

VenueTemáticas · 2014
Typearticle
Languagees
FieldSocial Sciences
TopicNationalism and Cultural Identity
Canadian institutionsnot available
Fundersnot available
KeywordsPhilosophyHumanitiesNationalismSociologyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Vivimos el siglo del nacionalismo y, paradójicamente, el del individualismo. El pensamiento sobre la colectividad y sobre el sujeto invade las reflexiones de las ciencias sociales de los últimos 100 años, principalmente luego del boom del psicoanálisis y del marxismo. Metodológicamente, la necesidad de que las ciencias sociales sean consideradas ciencias, en comparación con las ciencias naturales y su Método, ha generado una imposibilidad de pensar la realidad por fuera de sus aspectos racionales. La propuesta de este artículo es la de pensar el nacionalismo cívico, desde las experiencias de Cataluña y del Quebec, para hacer una propuesta teórico-metodológica que, en el cruce entre sociología, antropología, ciencias políticas e historia, nos permita pensar este fenómeno tan actual de una manera más profunda. El nacionalismo no es únicamente desarrollado por las instituciones políticas, sino que es, principalmente, una experiencia cotidiana, una forma de hacer y de pensar nuestra identidad: la nación se piensa, pero principalmente se vive, se baila, se siente.

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)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0060.021
Scholarly communication0.0070.007
Open science0.0020.005
Research integrity0.0020.004
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 head0.107
GPT teacher head0.458
Teacher spread0.350 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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