Security through emotions: narratives of temporal and spatial belongings of the Polish Territorial Defence Forces
Bibliographic record
Abstract
Since Russia’s annexation of Crimea, militarization processes have started to take place in central and eastern Europe. In Poland, one element of this development is the establishment of Territorial Defence Forces (TDF) in 2017. They are assigned to support their local communities, to strengthen patriotism, and to provide a feeling of security among the local population. Drawing from official websites and social media accounts of TDF as well as from national security policy documents, this article examines the role of emotions with regard to the TDF’s task as security provider. By combining literature on emotions and narratives of belonging with security conceptions, the article argues that TDF contribute to security less through their military strength than through the spatial and temporal belongings they offer publicly. Spatial narratives of belonging contain emotions of love and affection towards home regions and the state with the aim of translating them into territorialized security practices. Temporal narratives of belonging refer to the Home Army and “cursed soldiers,” revealing strong emotional attachments to armed resistance and thereby providing a role model for contemporary security production. Simultaneously, TDF express their non-belonging to the socialist past as a further element to produce security.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".