Constructing national identity: A qualitative analysis of separatism
Bibliographic record
Abstract
The study of public perceptions on nationalism continues to be problematic for sociology and the social sciences. For example, do the people of Catalan think of themselves first as being Catalonians, or are they Spanish or even European? Likewise, if the people have a hard time perceiving the true nature of their community identity, then external (i.e. international) public perceptions concerning the same agenda should be problematic as well. This dissertation limits its focus to external (international) media perceptions surrounding the social construction of national identity movements. The three national identity movements examined in this dissertation are Chiapas (Mexico), East Timor (formerly part of Indonesia), and Quebec (Canada). Theoretical basis for the study is drawn from Habermas' conceptualization of the public sphere (conflict theoretical approach), and is supported by the theoretical approaches of W. I. Thomas (definition of a situation), Berger and Luckmann's (social constructions of reality), and Foucault (post-modernist theory) to deconstruct international perceptions of the three social movements. A model is introduced to show the flow of social construction from competing factions within and outside a national identity movement to the outcomes that are produced and reported by international media sources covering aspects of social action. A sub-model is offered as a supplemental tool to the primary model, in order to organize (or deconstruct) the frame of major competing interests affecting social construction of the national identity movement cases. The study incorporated a qualitative methodology, which included assembling a data set consisting of 2,960 units (Chiapas N = 28; East Timor N = 1,030; and Quebec N = 1,902). Each data unit represented a separate international printed media source. Parameters for these data included a longitudinal range covering 1990--2000, and were coded into five major social institutional categories (political, economic, arts and entertainment, media and military). These categories were used to identify institutional shifts giving rise to the social construction of the three national identity movement cases during the given historical frame. Frequencies were tabulated for each institution category, by case, and by year to gain a better understanding of the longitudinal social construction of national identity.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".