dataesr/scientific-paragraphs-categorization huggingface dataset:
Cambridge Analytica used these matched personality scores for its voter profiling and targeted advertising services  (Federal Trade Commission, 2019a). According to Kaiser, Cambridge Analytica's "prodigious and unprecedented" database  (Kaiser, 2019b, p. 77)  included data from Facebook, data vendors, and (in the USA) their client's proprietary data that they had produced themselves and was not purchasable on the open market: as such, Cambridge Analytica held between 2,000 and 5,000 individual pieces of personal information on every person in the USA over the age of 18 years  (Kaiser, 2019b, p. 12-13, 78, 82-83). There are coercive elements to how this deceptively gathered data was then deployed. Speaking to the US context, Cambridge Analytica whistleblower, Christopher Wylie, testified about the company to the US Congress and the UK Parliament's Inquiry into Fake News and Disinformation  (Wylie, 2019).
EU, GoSkippy/Eldon Insurance (Banks' insurance companies) and UKIP. Kaiser raised concerns that the personal data of British citizens who merely wanted to buy insurance was being used for political purposes  (Kaiser, 2018b, p. 3 ) and expressed "similar concerns about whether UKIP members consented to the use of their data"  (Kaiser, 2018b (Kaiser, , p. 2, 2019b, p. 138), p. 138). Over a year later, in July 2019, Kaiser, supplied ten documents dated from 2015 to the Inquiry that showed that Cambridge Analytica had engaged in some work for Leave. EU on the EU referendum. This included analysis of UKIP's membership data (whose members are hard Eurosceptics), and analysis of survey results, to model four key groups of persuadable British voters to target with Leave. EU messaging: the "Eager Activist, " "Young Reformers, " "Disaffected Tories, " and "Left Behinds"  (Kaiser, 2019a, p.
51-52). In December 2019, whistleblower Dan Dennemarck, UKIP's former data controller, claimed that he had been ordered to hand over UKIP's database of over 100,000 current and exmembers to staff of Leave. EU during the referendum. David Soutter, UKIP's former head of candidates, also claims that a deal had been made as UKIP had faced financial meltdown following its unsuccessful election battle against the Conservatives in 2015: "UKIP was financially on the ropes. It was short of money. I understood at the time that an offer had been made by Arron Banks to take on that weight, to take on that role, and run the database. Run the membership function of UKIP"  (Howker, 2019). Whether or not chargeable work was done by Cambridge Analytica for Leave. EU, evidence suggests that Leave. EU greatly benefited from the work that went into Cambridge Analytica's pitch.
309; Shipman, 2017, p. 412). There are at least two ways, then, in which Leave. EU may have used the UKIP database (that, according to whistleblowers, Banks had purchased) to find other pro-Brexit voters via social media platforms: by using tools from social media platforms such as Facebook's Lookalike audience builder (as described earlier); and/or by copying Cambridge Analytica's modelling using Banks' actuaries from his insurance company. Such data and insights would complement activities by the US strategy firm (Goddard Gunster) hired by Leave. EU to engage in large scale telephone polling (between 25,000 and 100,000 people) to understand voters' concerns, followed by population segmentation, microtargeting identified groups, and then getting the vote out via "precision target-messaging"  (Banks, 2017, p.
A Cambridge Analytica document detailing the pitch states: The first part of this phase, which will be centred on a particular geographic area (like a parliamentary constituency), will involve a programme of Target Audience Analysis, whereby qualitative and quantitative research is conducted in order to segment the population into target audiences according to their views, motivations and interests. The second part of Phase II, Political Microtargeting, involves the use of secondary data sources and advanced analytics to assign values on particular traits to the entire voting population of the area in question. This will allow for the target audiences to be resegmented and contacted as required over the course of the campaign, and the use of this data will be facilitated by the deployment of an online database utility created by Cambridge Anlaytica for Leave.
Such behaviour change tools are coercive in that they aim to change people's behaviour via covert psychological manipulation rather than through freely chosen, deliberative decisions. Furthermore, Cambridge Analytica offered its profiling and micro-targeting services using far more data than that used by the Target Audience Analysis methodology developed for military psy-ops; and as noted earlier, a meta-analysis of relevant psychometrics research  (Azucar et al. , 2018)  points to capacity for covert behaviour change campaigns by those with access to multiple data streams. On top of this, when Cambridge Analytica pitched for Leave. EU's business, part of that pitch offered voter suppression-which is coercive in that it actively seeks to constrain people's choices in dampening their volition to vote.
Deceptive Features of Leave. EU's Campaign As noted earlier, Cambridge Analytica whistleblower Wylie, speaking to the US context, describes coercive (choice limiting) manipulation of potential voters by psychologically profiling them in order to target them with a provocative morass of digital disinformation, thereby significantly altering what they are exposed to in the digital media ecology. If Cambridge Analytica followed the same playbook in the Brexit referendum as Wylie claims that it used in the US elections, then it would also target psychologically profiled referendum voters with a morass of optimised digital disinformation. The absence of a complete database of digital ads from the 2016 referendum makes verification of any such claim difficult (Facebook only released ads to the Inquiry into Fake News and Disinformation from the other Leave groups rather than from Leave.
EU posted an advert depicting terrorists waving AK-47s captioned, "Islamist extremism is a real threat to our way of life. Act now before we see an Orlando-style atrocity"  (Banks, 2017, p. 282). A typical Leave. EU Facebook post warned voters that "immigration without assimilation equals invasion"  (Caesar, 2019). During the referendum campaign, Leave. EU also published a fake video as an "undercover investigation" on Facebook that went viral, with hundreds of thousands of views. The video claimed to show how easy it is to smuggle migrants into the UK from across the English Channel, but debunking this video several years later, satellite data shows that the footage was filmed in reverse  (Channel 4 News, 2019). Such deceptive messages delivered via Facebook to targeted, profiled audiences would be difficult for the wider population to discern and counteract.
Public Feeling on Deception and Micro-Targeting in Digital Political Campaigning This paper has discussed scholarship that points to capacity for covert behaviour change arising from digital campaigns by those with access to multiple data streams, by profiling individuals, and tailoring adverts automatically displayed in individual users' profiles based on personality. It has highlighted how Cambridge Analytica sought to deploy these techniques in the Leave. EU campaign, and how the Leave. EU campaign adapted modelling from Cambridge Analytica's pitch to engage targeted audiences with emotive, deceptive ads. Throughout his testimony to the Inquiry into Fake News and Disinformation, Nix, in defence of Cambridge Analytica's mode of operation, maintains that people are "not naïve" and understand how their data can be gathered, processed and used to support other campaigns  (Nix, 2018b, p.
For instance, "Nationbuilder" allows campaign managers to build profiles of supporters and activists, integrate that database with information from social media and electoral rolls, and organise canvassing according to target voters' location. "Ecanvasser" uses voter mapping, surveys, issue tracking and analytics to understand what voters are thinking in order to target them through emails  (Ecanvasser, 2020). By the UK's 2019 General Election, just over half of the UK public were aware of digital micro-targeting tools with a majority disapproving of these practices: however, there were still large proportions unaware. YouGov survey research in 2019 commissioned by ORG shows that although 54% of the UK population were aware of how political parties target or tailor adverts based on analysis of their personal data (political microtargeting), almost a third (31%) were not aware at all, or not very aware.
DISCUSSION Cambridge Analytica's profiling offered to the Leave. EU campaign in the 2016 Brexit Referendum had deceptive and coercive features. On deception, Cambridge Analytica's big data gathering for its psychographic micro-targeting lied to people about using their personal data, resulting in maximum permissible fines to both Cambridge Analytica and Facebook by the UK's Information Commissioners Office, and attempts to sue by the US Federal Trade Commission. Cambridge Analytica's modelling of UKIP data to generate four key groups of persuadable British voters to be targeted with Leave. EU messaging, was not declared to the UK's Electoral Commission during the referendum. Finally, Leave. EU created highly deceptive, Facebook ads and videos propagating harsh anti-immigration messages. On coercion, a core psy-ops methodology (Target Audience Analysis) designed to covertly generate behaviour change in target populations was developed by the defence arm of SCL Group; and Target Audience Analysis was deployed by the political arm of SCL Group for digital political campaigning purposes.
Cambridge Analytica's Chief Executive Officer, whistleblowers, and the psychology literature pronounce the efficacy of psychographic targeting when combined with big data analysis. Such behaviour change tools are coercive in intention: they aim to make people change their behaviour via covert psychological manipulation rather than through freely chosen, deliberated decisions. Furthermore, when Cambridge Analytica pitched for Leave. EU's business, part of that pitch offered voter suppression-a technique that it also employed in Trump's 2016 presidential campaign. Voter suppression is coercive in that it actively seeks to constrain people's choices in dampening their volition to vote. Finally, that voters were targeted both with deceptive Facebook posts propagating harsh anti-immigration messages and with in-person visits from Farage suggests that their digital filter bubbles and real-world experiences were manipulated to maximise their exposure to this particular information stream.
If micro-targeting means that the wider population are unaware of the deception and coercion, then the wider population are also disabled from having reflective and deliberative shared conversations about what political campaigners stand for, weakening the opportunity to correct false and/or emotive information. To put it more formally, the psy-ops like features of campaigns using psychographics and big data is evident in that psychographic targeting exploits (a) the cultural-psychological attributes of profiled target audiences and (b) the digital mass communication system (especially social media and databases) to deliver (c) optimised messages to influence or nudge the target audience (constituting the application of force in precluding opportunities for reflection and deliberation and in limiting peoples' choices by making them feel or behave in a certain way) (d) in a covert manner (the target audience is unaware of the manipulation) that (e) also lends itself to deception (because of its covert nature).
A limitation of this study is its inability to demonstrate that deployment of such psy-ops campaigning tactics actually influenced targeted voters. This, unfortunately, is a general weakness of media "effects" research on the effectiveness of such digital political campaigning tactics, given the opacity and targeted nature of the messages; the absence of an independent, publicly accessible database of all digital adverts deployed; the absence of information on who specifically received these targeted messages, and on what basis, as well as the extent of organic information diffusion; and the impossibility of determining which messages proved decisive in influencing people's vote  (Aral and Eckles, 2019; Marchal, 2020). However, these limitations do not invalidate efforts to understand attempted psy-ops in digital political campaigns.
g. Arzarello 2006; Duval 2006 ) that are easily available through the use of Dynamic Geometry Software (DGS). In the semiotic approach, it is well recognized that the coordination of multiple representations of a particular mathematical object in different semiotic registers is fundamental for understanding  (Arzarello 2006) , including the process of moving from one representation to another, in a different register ('conversion', according to  Duval)  or within the same register  ('treatment', according Duval). This ability to recognize these transformations is at the basis of meaning recognition in different representations (both in the same register and in different registers), as well as the ability to recognize two different objects in two similar representations in the same register  (Fischbein 1987).
In this article, we present data from Australian in-service mathematics teachers who designed MERLO items for student to undertakerelated to mathematical concepts in the national curriculum. The features of MERLO items are: 1) questions of varied complexity based on an understanding of mathematical meanings and the justifying of learners' reasoning; 2) different registers of representations; 3) a focus on mathematical meanings, rather than on calculations and procedures. We present more fully below the theoretical framework underlying the pedagogy of the use of these items. In this article, we present an analysis of the waysin which teachers and researchers collaborated to integrate MERLO items into the formers' practices. As a consequence, we consider such items as a boundary object  (Star 2010; Star and Griesemer 1989) : "Boundary objects are a sort of arrangement that allow different groups to work together without consensus"  (Star 2010, p.
2009; Robutti et al. 2019)  or in a wider context of research in education  (Monod-Ansaldi et al. 2019; Nizet et al. 2019). We place ourselves in this stream of research to analyze the effects of these interactions in the specific case of MERLO items. In analyzing data based on these items produced by teachers in professional development, we used two different kinds of technologies, termed representational infrastructures and communication infrastructures by  Hegedus and Moreno-Armella (2009). Belonging to the first type is the dynamic software GeoGebra, introduced to the teachers by the researchers at a certain point of their process of designing such items, as an instrument of exploration given to students implementing it in various parts. Representations of mathematical objects that would be static on paper become dynamic, which supports students in connecting meanings to oneanother.
Method This study was part of a larger design-based research project, in the sense of  Cobb et al. (2003). This larger project tested how MERLO items could be used in teachers' practices. We used design-based research because it was particularly suitable for studies that include iterative interventions that are complex, interactive, and reflexive in nature  (Schoenfeld 2006 ). The research presented here was inspired by Italian work on MERLO items at the University of Turin  (Arzarello et al. 2015b; Carante 2017). Since 2015, it has been drawing on data from the Meaning Equivalence Reusable Learning Objects (MERLO) project by researchers from the University of Turin in Italy and the University of New Englandin Australia. The people involved are researchers from these two universities, a PhD student from Italy, and in-service Australian teachers in a professional development program.
The discussions were based on design strategies, on their didactical choices, on their reasons based on the national curriculum and on assessment. In this article, we report on analyzed data from (a) the data from the questionnaire and (b) the messages exchanged during the on-line communication amongst the Australian educator and the mathematics in-service teachers, as well as the files attached (worksheets, MERLO items, written explanations and justification of the process of desiring such items, articles, etc. ; 713 messages and 154 attached files were included in the analysis). We chose, in this article, to focus on a particular mathematical subject, in the domain of geometry, for two reasons: first, the evolution of the items is significant with respect to the results of the actions on boundary objects, starting from a syntactic approach of the items and then going to a pragmatic level of description through a discussion of the meaning of the item within a school context.
In the data shown here, we observed ways in which mathematics teachers were engaged in professional development-in this instance, the design of their itemsusing the platform as a communication infrastructure with its affordances. The affordances of this infrastructure as a communication tool integrated the potentiality of the dynamism introduced by GeoGebra in the MERLO item and provided the teachers with opportunities to interact together quickly and directly on their tasks. The Australian teachers were taught about such items, how they are structured and how they can be used in class, and were also challenged in using these particular items in their classes. They were introduced to such items produced by an Italian community of teachers. The passage from Italian to Australian institutional contexts was not a simple use of Italian items by Australian teachers; it was something more.
Separate normalisation scale factors are used to constrain Z+jets processes in the 0L and 2L channels, while the normalisation scale factors related to W+jets processes are shared between the 0L and 1L analysis channels. To constrain the tt+jets process, on the other hand, each channel relies on its own independent normalisation scale factors. The normalisation scale factors are measured together with the signal strength modifier through a simultaneous fit to data in all control and signal regions for all of the analysis channels. The simulated diboson background is split according to the presence or absence of a Z boson decaying to a pair of charm quarks, labelling them as VZ(Z → cc ) if such a Z boson decay is present and VV+other otherwise. Whereas in the signal regions the BDT discriminator is used for the final signal extraction, in the control regions the shape of the CvsB distribution is used in the TT, HF, and CC regions, while that of CvsL is used in the LF region.
Systematic uncertainties Systematic effects can impact the shape of the BDT discriminant distribution for both analysis topologies, as well as the H candidate mass in the merged-jet topology and the distributions of the charm tagger variables in the resolved-jet topology analysis. The dominant uncertainties are associated with the normalisation of the background from the data control regions and the limited size of the dataset. The values of the rate parameters associated with the background normalisations range from ∼ 0. 65 to ∼ 2. 4 with uncertainties in the range of 10% to 35%. Additional systematic effects are related to the jet energy scale and resolution, which are treated as correlated between the large-and small-R jets. The efficiency to reconstruct and identify electrons and muons is measured in events with Z bosons decaying to electrons and muons via the tag and probe method  [95].
The uncertainties on these efficiencies range from 2 to 4%. The efficiency for events to pass the combination of triggers based on the presence of one or two leptons is measured using Z/γ * → ee and Z/γ * → µµ events via the tag-and-probe method  [95]. The uncertainties associated with this measurement typically range between 1 and 3%. The efficiency of the p miss T trigger is parametrised using a single-muon dataset and the uncertainties are typically below 2%. Theoretical uncertainties related to the cross sections and the p T spectra of the signal and backgrounds are also considered. In the resolved-jet analysis the systematic uncertainties in PDFs, and in the renormalisation and factorisation scales are treated as uncorrelated among the four flavour classes considered in the V+jets processes, as described in Section 5.
The cross section uncertainties are set to their theoretical predictions, while the systematics associated to the renormalisation and factorisation scales are obtained varying the parameters relative to these scales to 0. 5 and 2. 0 of their nominal values and estimating the effect. Lastly, the uncertainties in the c quark identification are also considered. The full list of systematic uncertainties is provided in Table  3. In Table  4  the uncertainty sources are grouped into categories and their impact on the fitted signal strength resulting from the combination of the resolved-jet and merged-jet topology analyses is provided (see Section 8 for more details). The uncertainty breakdown shows that the search for the VH(H → cc ) process is mainly limited by the size of the available dataset: the related uncertainty accounts for more than 85% of the total uncertainty in the fitted µ.
The statistical uncertainties include contributions from the limited number of events in the available dataset and the background normalisations extracted from the control regions. The main sources of systematic uncertainties come from the charm tagging efficiencies and the modelling of the simulated physics processes, representing ∼ 28% and ∼ 25% of the total uncertainty, respectively. The uncertainties in the theory prediction, which include uncertainties in the cross sections, p T spectra, PDFs, renormalisation and factorisation scales, also play a considerable role and represent approximately 30% of the total uncertainty in µ. Table  3 : Summary of the systematic uncertainties for each channel. The shape uncertainties refer to systematic uncertainties that affect both the shape of the distribution being fitted as well as their normalisation.
Summary In this paper, we present the first search by the CMS Collaboration for the standard model (SM) Higgs boson H decaying to a pair of charm quarks, produced in association with a vector boson V (W or Z). The search uses proton-proton collision data at a centre-of-mass energy of 13 TeV collected with the CMS detector in 2016 and corresponding to an integrated luminosity of 35. 9 fb -1. The search is carried out in five modes, Z(µµ)H, Z(ee)H, Z(νν)H, W(µν)H, and W(eν)H, with two complementary analyses targeting different regions of phase space. The signal is extracted by statistically combining the results of the two analyses. Each analysis is first validated by carrying out a search for Z boson decay to a cc pair and comparing the observed signal strength with the SM prediction. The Z boson signal strength for the combination of the two analyses is measured to be µ VZ(Z→cc ) = σ/σ SM = 0.