‘The All-American Smile:’ the deconstruction of Robert Redford’s star persona in David Lowery’s <i>The Old Man & the Gun</i>
Bibliographic record
Abstract
This paper considers the way Robert Redford’s star persona operates within David Lowery’s The Old Man & the Gun (2018), a contemplative deconstruction of the heist genre, that borrows a great deal from Redford’s persona, while deliberately subverting any elements that could be considered heroic. After establishing a theoretical framework based on the work of Richard Dyer and Richard deCordova, this paper surveys key Redford roles as it traces the parameters of what Dyer calls a star’s ‘structured polysemy’ or what deCordova defines as ‘symbolic identity’. A close reading of Lowery’s film reveals the way that Redford’s persona, and the white male romantic leads it represents, is challenged, problematized, and deconstructed through a blending of Redford’s persona into the life of Forrest Tucker, a septuagenarian bank-robber. The film’s frequent allusions to Redford’s career meld Tucker and Redford together as they expose the hollow nature of Tucker’s pursuit of masculine action. Redford, who was over eighty during production, embodies the futility of Tucker’s quest by being placed in the postures of his youthful roles (astride a horse, for example), offering an ironic contrast to his most iconic characters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".