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Record W259822864

The Dirt of E-Waste: Environmentalism Isn't Measured Only by Green Purchasing. A Healthy, Green Disposal Method Is the Back End of a District's Responsible Energy Plan

2009· article· en· W259822864 on OpenAlexaboutno aff
Dian Schaffhauser

Bibliographic record

VenueT.H.E. Journal Technological Horizons in Education · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsnobodyPurchasingVendorDirtWork (physics)Quarter (Canadian coin)BusinessOperations managementPaymentMarketingEngineeringTelecommunicationsComputer scienceComputer securityFinance
DOInot available

Abstract

fetched live from OpenAlex

POP QUIZ! What happens to your computer equipment when you've declared it surplus? Does it get shuffled into a warehouse, awaiting attention at some unspecified later date? Do you stick it on a pallet and have it hauled away by a recycler? Do you sell it, refurbish it, ship it back to a vendor, or drive it to the dump? Don't know? You're not alone. Most smart technology leaders can name multiple efforts they've already taken or expect to pursue in their schools to IT operations, such as powering off idle computers and virtualizing the data center. one area that many of them may not be so savvy about is hardware disposal: What to do with the old stuff? After all, it's not something from which they can garner easy or obvious savings. But, as some districts have figured out, the disposal end of technology acquisition is as vital a part of purchasing decisions as choosing energy-efficient devices. Nobody knows precisely how much e-waste is generated by schools nationwide. According to the Natural Resources Defense Council, Americans on the whole throw out about 130,000 computers a day. That tallies up to 47.5 million a year. And the numbers can only grow. Technology market researcher Gartner estimates that 15.6 million new PCs were shipped in the US during just the fourth quarter of 2008--and that was during an economic slowdown. It's safe to assume that the work of schools to refresh their technology contributes a fair share to that count. So what should you do when you don't want your old machines anymore? It isn't sufficient to simply say, Recycle! Those good intentions often come to bad ends. According to a study by the Silicon Valley Toxics Coalition, which advocates for a clean and safe high-tech industry, up to 80 percent of e-waste taken to recycling centers in this country ends up being exported to towns in developing countries for scrap recovery. There, according to a CBS 60 Minutes report last November titled Electronic Wasteland, residents, including children, use crude and toxic means to dismantle computers, monitors, and other electronics in an effort to remove precious metals, such as gold. That's antithetical to what US educators want, explains Sarah O'Brien, outreach director of the Green Electronics Council, a Portland, OR-based organization that works for the environmentally safe use and reuse of electronic products. O'Brien educates purchasers and the public about the GEC's EPEAT (Electronic Product Environmental Assessment Tool), a system that helps green-minded buyers by establishing criteria that identify just how green a computing device is. A lot of the criteria that have to do with toxics have a direct impact on kids, she says. Not [just] the kids in the district--children across the world. districts that approach the disposal of their old, unwanted computer equipment with the proper diligence are finding that they have several options, all of which illustrate why unloading e-waste doesn't have to be dirty work. [ILLUSTRATION OMITTED] Another School's Treasure Before the concept of e-waste recycling was better understood, Union School District in San Jose, CA, would rent giant waste containers at great expense. The bins would be labeled recyclable materials, recalls Mary Allen, supervisor of maintenance and operations. But back then nobody paid attention. All we were told was, 'You can't put concrete or dirt in there.' We dumped everything. When I first started with the district, we had piles and piles of this stuff, because nobody knew what to do with it. Once the district learned that monitors and TVs were hazardous waste, says Allen, it held on to them. The 4,000-student district picked up the disposal costs--about $1,000 dollars a year--until a company came along that offered to haul away the whole lot of electronics for free, including monitors, computers, copy machines, and printers. …

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)0.003
metaresearch head score (Gemma)0.009
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.009
Scholarly communication0.0210.021
Open science0.0010.006
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0260.011

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 head0.015
GPT teacher head0.279
Teacher spread0.264 · 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
GenreEmpirical

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
Published2009
Admission routes1
Has abstractyes

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Same venueT.H.E. Journal Technological Horizons in EducationSame topicRecycling and Waste Management TechniquesFrench-language works237,207