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Record W4245841628 · doi:10.32920/ryerson.14647788

The role of collective memory in solidifying identity : the case of Palestinian refugees

2021· preprint· en· W4245841628 on OpenAlexaff
Ghaida Hamdan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsToronto Metropolitan UniversityUniversity of Alberta
Fundersnot available
KeywordsCollective memoryRefugeeIdentity (music)Collective identityResistance (ecology)National identityPolitical sciencePalestinian refugeesSociologyGender studiesPolitical economyCriminologyLawAestheticsArt

Abstract

fetched live from OpenAlex

Through a critical review of scholarly literature on the role of collective memory as a resistance tool to memoricide, this research paper uses an analytical approach to examine the efforts exerted by Palestinian refugees in post 1948 Nakba (catastrophe of dispossession) to preserve and consolidate their national identity through the transmission of history of displacement and cultural heritage. With a specific focus on the fourth-generation Palestinian refugees living under occupation, and through studying cultural practices, oral history, and the Great March of Return Movement, this paper examines the role played by the collective memory in consolidating the Palestinians national identity in the post-Nakba era. The research argues that the collective memory constitutes a central anchor to preserving the Palestinian national identity and becomes an instrumental site for resistance, creating a generation of hope and rights, not despair and loss, as claimed by the recent literature. Key Words: Palestinian Refugees, Nakba, Collective Memory, Memoricide, National Identity, Oral History, Memory, Heritage, Occupied Palestinian Territory, Right of Return

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)0.005
metaresearch head score (Gemma)0.006
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0150.023
Scholarly communication0.0080.006
Open science0.0010.008
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.331
Teacher spread0.299 · 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
Published2021
Admission routes1
Has abstractyes

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Same topicJewish and Middle Eastern StudiesFrench-language works237,207