Queen Victoria and Prince Albert: Constructing a Sustainable Monarchy and Negotiating Gender and Foreignness Under Conditions of precarity
Bibliographic record
Abstract
Although many writers have commented on Queen Victoria’s role as a female monarch and the anxieties associated with female rule – authors such as Thompson (2001), Munich (1987) and Homans (1993) provide an in-depth account of Queen Victoria as a powerful female figure – there has been little discussion of Queen Victoria’s and Prince Albert’s reception as German figures of monarchical power. This dissertation examines the extent to which Queen Victoria’s and Prince Albert’s German heritage affected their public reception, particularly against the backdrop of political precarity. I argue that Queen Victoria and Prince Albert utilised royal iconography and portraiture in order to gain the approval of their subjects and to demonstrate that they were capable of sustaining the monarchy at a politically precarious time. To set the scene, the dissertation first discusses how far Britain/the British political system can be perceived as precarious in an age defined by revolutionary thought and reformatory action, before moving on to present the development of the nineteenth-century feminine ideal and its portrayal in English and German literature. My discussion of nineteenth-century gender ideals represented in royal portraiture demonstrates how such iconography was used by Victoria to construct an image of herself as the maternal monarch with a chivalrous German husband. In Chapter Three, I consider the royal couple’s split identities: Queen Victoria was a female figure of power with a German heritage linked in marriage to a German husband and male consort Prince Albert. The intersectionality associated with Victoria’s and Albert’s foreignness will be analysed in relation to Victoria’s and Albert’s seemingly “upside-down” or unconventional gender roles. I argue that Queen Victoria and Prince Albert used royal iconography, photography, and portraiture to manage their public reception and deflect public anxieties relating both to their foreignness and their gender unconventionality. Finally, I will reflect on the extent to which Victoria and Albert succeeded in constructing a sustainable monarchy under conditions of precarity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".