Bibliographic record
Abstract
This thesis examines French efforts to project their power onto Britain during the Middle Ages, engaging the Welsh as their partners. The subsequent chapters contribute fresh analysis on a range of leaders and periods. This has been done using new theories, particularly military ones, and pushes the boundaries of this area of studies. The concepts of ‘bracketing alliances’ and the strategy of ‘cultivate and eradicate’ have been introduced and applied to this research. In addition, the thesis includes works not commonly found in such a study; reaching outside the field to help clarify points of analysis. For example, Sun Tzu has been included to demonstrate that medieval rulers were practising the most effective methods of warfare, as we recognise them today. Also, by applying modern diplomatic theory, such as ‘Soft Power’, this research not only gives these ideas a wider conceptual use but also connects and makes relevant medieval events to the modern world. \nOn a broader level, these French-Welsh links demand wider exposure, whether from the perspective of the French attempting to articulate their power within Britain or the Welsh playing a role on the continental stage. This thesis presents new perceptions of these leaders, the conflicts of their times, their diplomatic initiatives and the power relations of the age. Its primary thrusts, therefore, are the dissection of the form and impact of these diplomatic and military relations, focussing on French efforts to project their power onto Britain at moments when friendship was co-ordinated with Welsh leaders. In recognition of the many subjects researched, and to borrow shamelessly from William of Malmesbury, and certainly others, I give note; ‘uolo enim hoc opus esse multarum historiarum breuiarium.’1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".