Identity Joyriding with the Trickster in Drew Hayden Taylor’s Motorcycles & Sweetgrass
Bibliographic record
Abstract
“A magician is an actor impersonating a magician.” - Jean Eugène Robert-Houdin In his numerous plays, short stories, essays, and novels, acclaimed Canadian/ Anishnawbe author Drew Hayden Taylor revels in toying with notions of identity construction, negotiation, and performance not only related to Natives and non-Natives but also to himself. In fact, those so-called fake identities in the form of imposters, con artists, and wannabes that populate his works can be viewed as recurring themes in the ongoing search for identity as well as authenticity. Initially, selecting a suitable character as my object of academic inquiry proved to be no easy task – until the trickster magically appeared. Or should I say reappeared? In Taylor’s most recent novel, Motorcycles & Sweetgrass (2010), the author makes little effort to obscure the return of the once ubiquitous trickster, previously known as “chief troublemaker and champion of Canada’s Native people,” but now a mere shadow of his former self in a world no longer filled with magic. Instead of reviving the trickster of yore, Taylor offers us a contemporary take on that fabled “fake,” who appears out of the blue and literally as well as figuratively unleashes a storm upon Otter Lake, a sleepy reserve in Ontario where apparently nothing noteworthy ever happens. At least that’s what the author would have us think at the beginning of his tumultuous tale; however, nothing is ever as it seems. In fact, that Sleeping Beauty of a reserve is just waiting for a wake-up-kiss from none other than this mysterious stranger who makes his grand entrance – not as Prince Charming on a white horse – but as a beguiling blond-haired biker on a 1953 Indian Chief motorcycle. The ever-changing identities of this mystery man begin to unfold rapidly as he embarks on joyride upon joyride on and off the reserve, changing his name as well as his game to suit the situation. As a result, he means different things to different people. Is he a good friend or an erotic drifter? Is he a duplicitous con man or a long-awaited savior? Can he possess a multitude of identities and still be authentic? In order to delve more deeply into these issues, Western society’s notions of truth and authenticity first need to be addressed. Secondly, the construction, negotiation, and performance of this “fake’s” multiple identities will be investigated from a psychological point-of-view, including an analysis of diverse aspects of imposture such as verbal mimicry and fluency, excessive expression of limited empathy as well a heightened sense of reality. Additionally, the con man’s use of choreographed performances and identity management in confidence games will be contrasted against the expectations and beliefs of his marks, i.e. the unwitting people with whom he interacts. Finally, I will assess to what extent the ambiguous archetype of the trickster – simultaneously a boundary crosser and boundary creator – could function as a go-between to offer new perspectives for delineating identity and redefining authenticity as well as to impart lessons about this very gray area in a dichotomy-focused society.
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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 | 0.014 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".