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Harold Innis’s Concept of Bias: Its Intellectual Origins and Misused Legacy

2021· reference-entry· en· W3217531970 on OpenAlexaffabout
Edward A. Comor

Bibliographic record

VenueOxford Research Encyclopedia of Communication · 2021
Typereference-entry
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsWestern University
Fundersnot available
KeywordsEpistemologyScholarshipSociologyDeterminismSocial sciencePolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

Abstract Harold Innis is one of the foundational theorists of media and communications studies. In the mid-20th century, he developed his concept of media bias (also called the bias of communication). It remains Innis’s most cited concept, but it is also significantly misunderstood. For example, since his death in 1952, bias has often been applied in ways that are akin to a form of technological or media determinism. This has been an ongoing problem despite the fact that Innis developed his concept as a means of compelling analysts to reject such mechanistic formulations. Indeed, his goal was to promote more self-reflective modes of scholarship and, by extension, a recognition that such intellectual capacities—which he believed were essential for civilization’s survival—would be lost if they were not recognized and defended. More generally, Innis contextualized his work regarding media bias in terms of interrelated historical conditions involving political economic dynamics. Through his application of the concept to over 4000 years of history, he sought to provide his contemporaries with the reflective perspective needed to comprehend the underpinnings of modern biases that stressed present-mindedness and spatial control to the neglect of creativity and duration. Bias was derived from Innis’s studies on Canadian economic development involving the exploitation of its resources (an approach to history called the staples thesis). Several of the insights he garnered in those studies need to be recognized if we are to fully understand his subsequent communications research. Also, tracing the origins of his concept of bias enables us to fully assess the nature of Innis’s supposed media determinism. Typical uses of bias today focus on the spatial or temporal orientations that many assume are compelled through the use of a specific medium or set of media technologies. This misreading, inspired mainly by Marshall McLuhan’s representations of Innis, has led to assumptions regarding Innis’s determinism and a general neglect of the complexity of his original work. To repeat, Innis developed bias in order to redress the mechanistic and unreflective thinking of his day and always conceptualized it in terms of factors that are salient to the place and time being examined. Moreover, he applied bias alongside now largely forgotten concepts ranging from unused capacity to classic power–knowledge dialectics. Lastly, he situated the development and implications of a particular medium in relation to both old and new media (not just technologies, but organizations and institutions also). In sum, to comprehend Innis’s concept of bias, its intellectual and political underpinnings need to be acknowledged, the political economic dynamics of its development and application understood, and the implications of McLuhan’s influence recognized.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.005
Science and technology studies0.0070.070
Scholarly communication0.0130.017
Open science0.0020.007
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.154
GPT teacher head0.409
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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