Perceptions of Time, Cultural Boundaries and ‘Region’ in Early Indian Texts
Bibliographic record
Abstract
Historiographical positions have hitherto suggested that historical regions be objectively encapsulated solely as entities of political control or, as regions with their present-day linguistic boundaries. This article takes an in-depth look at the way notions of time, history, space, boundaries and identities evolved in the early Indian textual traditions that impinged on how regions were continually in the process of making. Critical to the argument is the unveiling of theoretical underpinnings of the sources that modern historians use to reconstruct ancient historical regions, states and territories. Next, it highlights the boundaries of socio-cultural regions, as specified in the dominant literary tradition, to conclude that an inherent fluidity was manifested especially in the reckoning regions of exclusion. Stable definition of regions was, however, entwined in data emerging out of regional inscriptions that elaborated primarily on socio-economic mechanisms of control. In this case study, the early textual traditions culturally interlinked a locality and region to its large whole, whereas specific data from inscriptions projected more concrete realities of boundary, space and time. Conflict between the two modes of perceiving and documenting the past has to be reckoned with, so as not to project our modern concerns of ‘country’, ‘region’ and ‘history’ into the past.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".