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Record W3123167477 · doi:10.55016/ojs/sppp.v11i1.43356

Surviving and Thriving in the Digital Economy

2018· article· en· W3123167477 on OpenAlexaboutno aff
Goran Samuel Pesic

Bibliographic record

VenueThe School of Public Policy Publications · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsThrivingDigital economyBusinessEconomyComputer scienceEconomicsSociologyWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

Cyber-crime is growing exponentially and Canadian governments at all levels have not kept pace quickly enough to protect both themselves and private enterprise. Evolving technology allows for ever-more sophisticated cyber-threats to intellectual property, but some businesses and governments have neither changed their pre-internet thinking nor established adequate safeguards. Protection should start with educational campaigns about the scope and varieties of risk that permeate the private sector, e-commerce and smart cities using the internet of things. Thirty years ago, just 32 per cent of the market value of Standard & Poor’s 500 companies was based on intangible assets, mainly intellectual property. Today, that figure stands at 80 per cent and protecting those assets from cyber-crime is of vital importance. While cyber-criminals look to make money off of phishing scams, their interests have also extended to infiltrating proprietary industrial designs, resource management and information affecting acquisitions. The fact that some countries see this type of crime as a normal way to gain access to foreign business information is often poorly understood by Canadian businesses accustomed to functioning under much higher ethical standards. The e-commerce realm faces its own cyber-threats including those affecting privacy, data sovereignty, location of data centres, data security and legislation. E-commerce merchants must protect themselves by ensuring the security of their clients’ computers, communication channels, web servers and data encryption. It sounds daunting, but it shouldn’t be. Merchants can take steps such as doing risk assessments, developing security policies, establishing a single point of security oversight, instituting authentication processes using biometrics, auditing security and maintaining an emergency reporting system. Government can assist with cyber-security in Canada’s private sector through awareness campaigns, rewarding businesses for best practices, providing tax credits to offset the cost of security measures, and offering preferential lending and insurance deals from government institutions. The federal government’s 2015 Digital Privacy Act was a good first step, but there is much territory left to be covered. The act offers little assistance in making the leap from a pre-internet governmental model of doing business with the private sector. Nor does it acknowledge the full costs organizations must face when contemplating improving their cyber-security. The growth of smart cities, connected to the internet of things, creates new susceptibilities to cyber-crime. By 2021, there will be approximately 28 billion internet-connected devices globally and 16 billion of those will be related to the internet of things. However, smart cities appear to be low on the list of cyber-security priorities at all levels of government. There is a lack of local guidance and commitment, an absence of funding programs and tax incentives for risk-sharing arrangements, and nothing in the way of a federally initiated smart-cities strategy. The key to keeping ahead of the cyber-criminals is to recalibrate our understanding of the threats accompanying the technology. New ideas, new economic policies, new safeguards, new regulations and new ways of doing business will all help to keep Canada safe in the burgeoning knowledge economy.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0130.015
Scholarly communication0.0190.017
Open science0.0010.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0160.006

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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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