Who Wrote the Elder Scrolls?: Modders, Developers, and the Mythology of Bethesda Softworks
Bibliographic record
Abstract
This paper considers the part played by modders in shaping Bethesda Softworks’ The Elder Scrolls series of roleplaying games. It argues that Bethesda’s stewardship of the franchise over the course of its twenty year history has been characterised less by an unwavering creative vision than a willingness to make use of the resources to hand - not least the inventiveness of modding communities. Charting how Bethesda employees and the games’ modders have performed and discussed their respective roles, we track shifts in the tools, vocabularies, aims and approaches of both parties. We find that while the practices and priorities of modders and developers have, in many respects, converged over this period, crucial legal and conceptual distinctions continue to separate professionals from amateurs. Valve’s abortive attempt to introduce paid mods to The Elder Scrolls V: Skyrim threw this division into stark relief, emphasising the need for studies of modding which address the performativity of intellectual property, showing how conceptions of authorship and ownership develop over time within specific studios, cultures and publics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".