Notice bibliographique
Résumé
Introduction The explosive growth in the wireless communication industry in the last two decades rests upon one all-encompassing truth for wireless providers: without access to spectrum, you cease to exist. Wireless broadband service cannot be provided without making use of the invisible, finite, publicly owned frequencies that serve as the conduit for mobile devices. For many regulators and industry officials, it has become an accepted maxim that industrial societies are nearing a spectrum capacity crunch. Industry has argued for years that the explosive growth of mobile data, triggered by the adoption of smart phones and tablets and the popularity of streaming video, has meant that they must have exclusive access rights to increasingly large portions of the public airwaves in order to provide adequate service. Since the 1990s this allocation of spectrum has largely been done via auction. Dallas Smythe wrote that the radio spectrum “does not rest with particular individuals or nations, but with all humanity” (D. Smythe, 1987). Smythe was the first vocal opposition to the concept of spectrum auctioning when it first appeared as a theory in the 1950s (Dallas W. Smythe, 1952; Dallas Walker Smythe, 1957; Taylor, 2013). He remained steadfast in his opposition to auctions as a method of spectrum allocation, even as they gained wider acceptance. Reflecting on that 1952 paper decades later, Smythe described it as “an article that I could not have improved 35 years on” (D.W. Smythe & Guback, 1994). The wireless industry has been unequivocal that it cannot provide service unless larger areas of the public spectrum are opened to them. The entire drive to auction spectrum is predicated on the idea that spectrum is increasingly limited. However, despite its fundamental importance to our increasingly mobile world, this scarcity is rarely quantified. There is increasingly reason to question this theory. This paper presents the results of a unique project measuring spectrum usage in Canada’s urban core. In doing so, I explore the legitimacy of questions of spectrum scarcity using a test case in Canada's most populous city. I also explore spectrum policy approaches beyond the auction of private licenses as ways of strengthening public wireless access. This paper explores the effects of 20 years of spectrum auctioning and places Smthe’s theory in the context of the current environment. Using Smythe as the theoretical foundation, I ask if auctions are serving the public interest. Methods The first section of the paper draws upon the theory introduced by Smythe in the late 1950s that auctions were a poor allocation method for the public spectrum. Smythe’s position was later supported by fellow Canadian communication scholar William Melody (Melody, 1980). This paper also draws upon work of Eli Noam (Noam, 1997), Yochai Benkler (Benkler, 2012) and the conclusions of a report prepared for the President of the United States in 2012 (President’s Council of Advisors on Science and Technology, 2012) to argue for a more commonist approach to spectrum access. The second part of the paper offers unique quantitative data addressing the issue of spectrum capacity in Canada. Working with Ryerson University’s Department of Electrical and Computer Engineering, I collected data on licensed mobile broadband frequencies using a spectrum analyzer. Data was collected hourly for each licensed band in twelve-hour shifts over three days. The spectrum analyzer registered activity for a band in the top chart and density of usage in the bottom chart. Results and Discussion The data clearly demonstrates the inefficiencies of paired spectrum allocation – a common approach in the allocation of this resource. The research discovered large discrepancies between the transmission frequencies and reception frequencies. In short: the data demonstrates that the allocation of this public resource is poorly structured, allowing for large amounts of prime public spectrum to sit unused by incumbents. Conclusions Questions of spectrum management should not be the exclusive realm of engineers. Scarcity is a fundamental element of classical political economy. In the Wealth of Nations, Smith writes that scarcity is the underpinning of almost all economic exchanges, from labour, rent, to agriculture and precious metals. The spectrum scarcity argument has been allowed to exist largely unchallenged for the last two decades. This paper questions the scarcity argument and uses the theoretical foundation offered by Dallas Smythe to probe into greater questions of spectrum and the common good. Acknowledgments Dr. Catherine Middleton, Ryerson University; Dr. Xavier Fernando, Ryerson University; Social Science and Humanities Research Council of Canada (SSHRC) Insight Grant. References and Notes Benkler, Y. (2012). Open Wireless vs. Licensed Spectrum: Evidence from Market Adoption. Harvard Journal of Law and Technology, 26(1). Melody, W. H. (1980). Radio Spectrum Allocation: Role of the Market. The American Economic Review, 70(2), 393-397. Noam, E. (1997). Beyond spectrum auctions. Taking the next step to open spectrum access. Telecommunications policy., 21(5), 461. President’s Council of Advisors on Science and Technology. (2012). Report to the President: Realizing the Full Potential of Government-Held Spectrum to Spur Economic Growth. Retrieved from http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast_spectrum_report_final_july_20_2012.pdf. Smythe, D. (1987). Radio Spectrum Policy and World Needs. CPRO Prometheus, 5(2), 263-283. Smythe, D. W. (1952). Facing Facts about the Broadcast Business. The University of Chicago Law Review, 20(1), 96-106. Smythe, D. W. (1957). The structure and policy of electronic communication. Urbana: University of Illinois. Smythe, D. W., & Guback, T. H. (1994). Counterclockwise: perspectives on communication: Westview Press. Taylor, G. (2013). Oil in the Ether: A Critical History of Spectrum Auctions in Canada. Canadian Journal of Communication, 38(1).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,014 |
| Communication savante | 0,011 | 0,007 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,010 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,038 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».