Historical consciousness and metaphor: Charting new directions for grasping human historical sense-making patterns for knowing and acting in time
Bibliographic record
Abstract
In adding on to narrative as one dominant means of studying and analysing expressions of historical consciousness, this paper attempts to explicate two potential roles of metaphor for fully capturing human historical sense making patterns as they pertain to living life. By bringing together cognitivist viewpoints regarding conceptual metaphors and their underlying mappings of core life concepts with more literary uses of metaphor as a central means of re-describing reality through paranarrative readings of textual extracts, a potentially novel way of looking at the operations of historical consciousness emerges – one where conventionalized conceptual metaphors underlying the logic of history seem to embed the conditions under which individuals either rely on pre-given significations of the past for knowing and acting in time, or rather seek plausible-like meanings instead. The author illustrates his ideas through an analysis of Milan Kundera’s embellished commentary on the ironies regarding the politics of remembering and forgetting during Czechoslovakia’s communist period in The Book of Laughter and Forgetting. In recognizing the experimental nature of his endeavour, the author nonetheless calls for further exploration and empirical research, particularly with real world human participants, to develop metaphor as a respected medium of research in the area of historical consciousness.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.076 |
| Scholarly communication | 0.014 | 0.034 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".