Crowdsourcing and small business challenges: how to leverage crowdsourcing benefits in the information and communications technology industry
Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) make significant contributions to economic growth; however, they face multiple challenges that inhibit their success. It is argued in this paper that crowdsourcing could be leveraged to alleviate many of their challenges particularly in the information and communications technology (ICT) industry. Our findings show that Canadian SMEs are using crowdsourcing less than their American counterparts. However, Canadian SMEs have a more optimistic outlook towards crowdsourcing impacts. Additionally, ten categories of Canadian SME challenges were identified through a review of literature. Finally, a compelling argument for leveraging crowdsourcing to address eight of these challenge categories is made, relying on existing literature and case studies of SME's utilisation of crowdsourcing. SME owners could leverage the findings from this paper to improve their chance of survival. Policy makers could also see benefits in guiding the design of new policies aimed at supporting small businesses.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".