Positioning Pooh: Edward Bear after 100 Years ed. by Jennifer Harrison (review)
Bibliographic record
Abstract
Reviewed by: Positioning Pooh: Edward Bear after 100 Years ed. by Jennifer Harrison Sarah Minslow (bio) Positioning Pooh: Edward Bear after 100 Years. Edited by Jennifer Harrison. University Press of Mississippi, 2021. This collection of essays offers a reflection on and extension of much of the existing scholarship on A. A. Milne’s Winnie the Pooh stories by scholars such as Paula Connelly, Peter Hunt, Ann Thwaite, and Daphne Kutzer. As Harrison points out in the introduction, which also serves as an overview of existing Pooh scholarship, most Pooh criticism has focused on biographical approaches, revealing hidden meanings, language play, power dynamics related to gender and colonization, in particular, and the role of Disney in transforming and perpetuating the legacy of the characters who inhabit the Hundred Acre Wood. As Harrison points out, [End Page 106] this book offers new approaches that highlight the relevance of Milne’s tales “not only because of their enduring place in popular culture but also because they encourage and present diverse, unstable, and flexible perspectives, interpretations, and modes of being in keeping with the global, postmodern, posthuman ethos of the twenty-first century” (xii). Several of the chapters examine the role of nostalgia for childhood innocence and play and the ways in which Derrida’s concept of “hauntology” bears on the stories of Winnie the Pooh and friends. David Rudd explains that the concept suggests we cannot possess things, “we only ever possess traces of things” (7). Zoe Jaques analyzes two recent cinematic versions of Pooh—Goodbye Christopher Robin (Fox Searchlight 2017) and Christopher Robin (Disney 2018)— through a similar lens of the perpetual image of a boy and his bear playing in the enchanted woods. Jaques argues that “Both films enact a form of rereading, or returning, to this text from a new position—a reorientation of the stories and their foundations” and offer readers what she calls “spectral nostalgia . . . a wistful looking back to the past, certainly, but via a backward glance that is specifically attentive to its hauntings and echoes” (49, 50). Niall Nance-Carroll examines two book adaptations, Return to the Hundred Acre Wood (2009) and The Best Bear in All the World (2016). Nance-Carroll argues that The Best Bear in All the World suggests a “more cyclical relationship with time” and fulfills some “yearning for return to an unchanging place” that could be called “restorative nostalgia” as opposed to “reflective nostalgia” (68). The restorative nostalgia found in The Best Bear is due to the fact that the narrative does not offer a conclusion and Christopher Robin does not grow up. As Nance-Carroll explains, “None of these stories challenge the continuity of the world established in the series, and none of them expand it” (71). This chapter also provides some insights into how the introduction of new characters, Lottie and Penguin, attempt to “add something that was missing from the early Pooh stories” (75). While the dynamics between characters are examined in Nance-Carroll’s chapter, Sarah E. Jackson explores how the rhetoric of classification is used throughout the original stories “to both colonizing and anticolonizing ends” (152). Jackson explains that the characters use classification “to appear knowledgeable and therefore in control of their environment; to establish a hierarchy; and to exert power over others” (152). Her most convincing example is an episode when Rabbit tries to “unbounce” Tigger, the newcomer, but is forced to realize with Pooh’s help that as long as Tigger is bigger than Rabbit, Rabbit “cannot truly control the other animals” (161). Several chapters focus the dynamics outside the book, between child and adult, reader and author, and humans and non-humans. Varga argues that “the anthropomorphizing of Winnie [Winnipeg, the real bear] has resulted in the substitution of her corporal and sentient being with cartoon imagery” and as a result “place[s] limitations on how humans [End Page 107] think about them [real bears]” (20–21). Varga encourages readers to consider that many of the festivals held in Canada in honor of the real bear who inspired the Pooh stories have worked to minimize her life so that now she is more of an “anthropomorphized commodity” (28). This critical animal studies approach...
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".