Discourse, National Identity and National Holidays: An Analysis of the Discursive Constitution of Israeli National Identity
Bibliographic record
Abstract
This dissertation analyses texts produced on and around three Israeli national holidays as sites at which national identity is (re)produced. I deploy a poststructural discourse analysis to examine how the representations of Israeli national identity on these holidays organize the construction of Israeli national identity between 2003 and 2013. I develop my arguments through analysis of a large dataset consisting of 1,316 pieces of evidence, made up of statements delivered by political elites as well as articles printed by the Jerusalem Post and Haaretz newspapers on or around the national holidays. I begin with a review of the contributions of poststructural International Relations scholarship and then outline my own contributions to this literature. I then develop my theoretical framework and outline my methods of data collection and management as well as data analysis. Before proceeding to the analysis of Israeli national identity, I review four other cases (Australia, Canada, New Zealand and the USA) that share similar national holidays to Israel. Such analysis emphasizes the significance of national holidays to understanding identity in global politics. I identify three major identity themes, and a corollary group of counter-themes, which emerge through the body of texts. The first theme I engage with asks how certain physical practices are enacted on national holidays and how these form part of Israeli national identity. I then pay particular attention to the practices of Holocaust memorialisation as a central organising principle of national identity. I proceed to examine how ideas of territory and nation are each produced throughout the national holidays to construct national identity. The final analytical chapter examines the discontinuities and ruptures of the three previous analytical chapters. I explore how resistances, concealments and counter-discourses are always present in identity discourses. I conclude with an appraisal of the key contributions that this work makes to disciplinary knowledge and how it can enhance the ways that poststructural IR scholars understand the role of national holidays in the construction of national identity.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".