MétaCan
Menu
Back to cohort
Record W7115950160 · doi:10.5604/01.3001.0055.5204

Telling „Nothing” about „Nobody”. The Specificity of Polish Therapeutic Narratives in the Last Years of the First Quarter of the 21st Century

2025· article· W7115950160 on OpenAlexaboutno aff

Bibliographic record

VenueTekstualia · 2025
Typearticle
Language
FieldSocial Sciences
TopicDiverse Academic Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeQuarter (Canadian coin)CynicismFeelingPoliticsCharacter (mathematics)Happening

Abstract

fetched live from OpenAlex

Text Telling „nothing” about „nobody”. The specifi city of Polish therapeutic narratives in the lastyears of the fi rst quarter of the 21st century presents on two prose examples (Hanka by MaciejJakubowiak and Magiczna rana by Dorota Masłowska) literary traces of two signifi cant processesrelated to the situation of political and economic transformation in Poland after 1989. The fi rstone was evidenced in specifi c stories about the social advancement of people from families withpeasant, less often proletarian, roots, in which the main character ultimately turned out to be thenarrator-sender of the text. The second one focused attention on the losers of the transformation,weak entities that were pushed to the margins of reality in the dynamics of change. The authorof the text puts forward the thesis that stories about this topic can be emotionally costly. Consciouslyor unintentionally, they trigger, among other things, also ugly feelings (Sianne Ngai’s term) – suchas jealousy, irritation, cynicism – and those who feel them may require therapy.

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)0.003
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.014
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.333
Teacher spread0.296 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueTekstualiaSame topicDiverse Academic Research StudiesFrench-language works237,207