Bibliographic record
Abstract
Travel and colonialism Travel and travel narratives shaped the way we understand the colonial and postcolonial world, and their importance to postcolonial studies has generated several book-length accounts in recent years. Colonialism encompasses the stories of many kinds of travellers with many motives, European merchant venturers, colonial officials, explorers, missionaries, settlers and others become bound together with people of the colonized spaces, who themselves, as we will see, engaged in travel between their homelands and the world beyond. Colonization may have begun, as was once remarked of the acquisition of the British Empire, ‘in a fit of absent-mindedness’, but it rapidly evolved into a way of consolidating these encounters in an emerging structure of conscious power and dominance. In the same way the early, random stories of encounter, which emerged as Europeans moved out to new lands, rapidly evolved into accounts that sought to impose European patterns and ideas on the experience of their expanding physical world. A full account of colonial travel literature might best begin by analysing some of the early ways the world was represented by these first random travellers beyond the then known world and how their narratives both shaped the imaginations of those who followed and inspired their curiosity and their cupidity. It might also consider how travel narratives began to shift the perspectives of Europeans as they began to embrace the wider horizons the travellers and their accounts brought home.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".